U.S. patent application number 14/384058 was filed with the patent office on 2015-06-18 for genotyping test for assessing risk of autism.
The applicant listed for this patent is Integragen. Invention is credited to Jerome Carayol.
Application Number | 20150167082 14/384058 |
Document ID | / |
Family ID | 49117444 |
Filed Date | 2015-06-18 |
United States Patent
Application |
20150167082 |
Kind Code |
A1 |
Carayol; Jerome |
June 18, 2015 |
GENOTYPING TEST FOR ASSESSING RISK OF AUTISM
Abstract
The invention relates to a method of determining a risk of, or
of detecting the predisposition to or the presence of autism in a
subject, the method comprising detecting the combined presence of
risk-associated SNP alleles at multiple loci in a sample from said
subject, which method comprises genotyping a single nucleotide
polymorphism (SNP) in the gene loci of at least HTR5A, MACF1,
RBFOX1, ABR, PTPRG, CACNA2D1, GFRA1, DSCAML1, CHRM3, LPPR4, DLG2,
SLC9A9 and BASP1.
Inventors: |
Carayol; Jerome; (Montrouge,
FR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Integragen |
Evry |
|
FR |
|
|
Family ID: |
49117444 |
Appl. No.: |
14/384058 |
Filed: |
March 8, 2013 |
PCT Filed: |
March 8, 2013 |
PCT NO: |
PCT/EP2013/054757 |
371 Date: |
January 15, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61608717 |
Mar 9, 2012 |
|
|
|
Current U.S.
Class: |
506/2 ;
506/16 |
Current CPC
Class: |
C12Q 2600/172 20130101;
C12Q 2600/156 20130101; C12Q 1/6883 20130101 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 9, 2012 |
EP |
12305285.4 |
Claims
1. A method of determining a risk of developing autism in a
subject, the method comprising obtaining a biological sample from a
subject and detecting in the biological sample a number of
autism-associated risk alleles by genotyping a single nucleotide
polymorphism (SNP) in the gene loci of at least HTR5A, MACF1,
RBFOX1, ABR, PTPRG, CACNA2D1, GFRA1, DSCAML1, CHRM3, LPPR4, DLG2,
SLC9A9, and BASP1 in the biological sample, wherein the SNP in
HTR5A is rs893109, the SNP in MACF1 is rs260969, the SNP in RBFOX1
is rs12925135, the SNP in ABR is rs2663327, the SNP in PTPRG is
rs636624, the SNP in CACNA2D1 is rs2367910, the SNP in GFRA1 is
rs10787637, the SNP in DSCAML1 is rs695083, the SNP in CHRM3 is
rs10802802, the SNP in LPPR4 is rs712886, the SNP in DLG2 is
rs12275631, the SNP in SLC9A9 is rs3928471 and the SNP in BASP1 is
rs298542, or a SNP in each of the gene loci in linkage
disequilibrium with each of the aforementioned SNPs with an
r.sup.2.gtoreq.0.80; the genotyping is carried out by sequencing,
selective hybridization, or selective amplification; and the risk
of developing autism is determined based on the number of
autism-associated risk alleles detected in the biological
sample.
2. The method of claim 1, further comprising detecting
autism-associated risk alleles by genotyping: a SNP in the gene
loci of any or all of KCNIP1, UGCG, NTRK3, PLCB1, NELL1, GPR98,
MAGI2, PLAGL1, CNTN6, DLG4, ERC2, TRIM9, SYT14, JARID2, CDH13,
SULF2, GRIN2A and NRG3, or combinations thereof; or a SNP in the
gene loci of any or all of NRG1, TRIM2, EPHA5, PCDH10, HIP1, APBA1,
PDE4D and EGLN3, or combinations thereof.
3. The method of claim 1, further comprising detecting
autism-associated risk alleles by genotyping at least one SNP in
the gene loci selected from the group consisting of ACCN1, AKAP7,
APBA1, ASTN2, CADM1, CDH13, CNTN6, DCLK1, DCLK2, DLG4, EGLN3,
EPHA5, ERC2, GPR98, GRIN2A, GRIN2B, GRM7, HIP1, JARID2, KCNH5,
KCNIP1, MAGl2, MAP1S, MAP2K1, NAV2, NELL1, NRG1, NRG3, NTRK3, PAX2,
PCDH10, PDE11A, PDE4D, PLAGL1, PLCB1, PTPRD, RGS6, SLC24A2, SULF2,
SYT14, TRIM2, TRIM9 and UGCG, or combinations thereof.
4. (canceled)
5. The method of claim 2, wherein the SNP in KCNIP1 is rs12514116,
the SNP in UGCG is rs16916456, the SNP in NTRK3 is rs7172184, the
SNP in PLCB1 is rs8123323, the SNP in NELL1 is rs10766739, the SNP
in GPR98 is rs16868972, the SNP in MAGl2 is rs12535987, the SNP in
PLAGL1 is rs2076683, the SNP in CNTN6 is rs9837484, the SNP in DLG4
is rs314253, the SNP in ERC2 is rs1485677, the SNP in TRIM9 is
rs10150121, the SNP in SYT14 is rs7534723, the SNP in JARID2 is
rs9370809, the SNP in CDH13 is rs9940922, the SNP in SULF2 is
rs6063144, the SNP in GRIN2A is rs4782109, the SNP in NRG3 is
rs2820100 or rs7075400, the SNP in NRG1 rs723811, the SNP in TRIM2
is rs11942354, the SNP in EPHA5 is rs1597611, the SNP in PCDH10 is
rs4404561, the SNP in HIP1 is rs6962352, the SNP in APBA1 is
rs11139294, the SNP in PDE4D is rs35284, and the SNP in EGLN3 is
rs946630.
6. (canceled)
7. The method of claim 3, wherein the SNP in KCNH5 is rs1041644,
the SNP in MAP1S is rs12985015, the SNP in GRM7 is rs1569284, the
SNP in PAX2 is rs2077642, the SNP in PTPRD is rs2382104, the SNP in
PDE11A is rs2695112, the SNP in RGS6 is rs6574041, the SNP in ASTN2
is rs7021928, the SNP in ACCN1 is rs7225320, the SNP in DCLK2 is
rs9307866, the SNP in SLC24A2 is rs957910, the SNP in AKAP7 is
rs6923644, the SNP in DCLK1 is rs1556060, the SNP in MAP2K1 is
rs1432443, the SNP in CADM1 is rs220836, the SNP in GRIN2B is
rs7974275, and the SNP in NAV2 is rs10500866.
8. (canceled)
9. The method of claim 1, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of PITX1, ATP2B2, EN2, JARID2, MARK1, ITGB3, CNTNAP2,
and HOXA 1, or combinations thereof.
10. A method of determining a risk of developing autism in a male
subject, the method comprising obtaining a biological sample from a
male subject and detecting in the biological sample a number of
autism-associated risk alleles by genotyping SNPs in the gene loci
of at least HTR5A, MACF1, RBFOX1, ABR, PTPRG, and CACNA2D1, in the
biological sample, wherein said SNPs are rs893109, rs260969,
rs12925135, rs2663327, rs636624 and rs2367910 or SNPs in linkage
disequilibrium with each of the aforementioned SNPs with an
r.sup.2.gtoreq.0.80; the genotyping is carried out by sequencing,
selective hybridization, or selective amplification; and the risk
of developing autism is determined based on the number of
autism-associated risk alleles detected in the biological
sample.
11. The method of claim 10, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of KCNIP1, UGCG, NTRK3, PLCB1, NELL1, GPR98, MAGl2,
and PLAGL1, or combinations thereof.
12. The method of claim 10, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of NRG1, TRIM2, EPHA5, PCDH10, and HIP1, or
combinations thereof.
13. The method of claim 10, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of PDE11A, AKAP7, DCLK1, KCNH5, GRIN2A, ACCN1, DCLK2,
ASTN2, GRM7, MAP2K1, CADM1, and GRIN2B, or combinations
thereof.
14. The method of claim 10, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of PITX1, ATP2B2, EN2, JARID2, CNTNAP2, and HOXA 1,
or combinations thereof.
15. A method of determining a risk of developing autism in a female
subject, the method comprising obtaining a biological sample from a
female subject and detecting in the biological sample a number of
autism-associated risk alleles by genotyping SNPs in the gene loci
of at least CHRM3, DSCAML1, PTPRG, GFRA1, LPPR4, DLG2, SLC9A9 and
BASP1, in the biological sample, wherein said SNPs are rs10802802,
rs695083, rs636624, rs10787637, rs712886, rs12275631, rs3928471 and
rs298542 or SNPs in linkage disequilibrium with each of the
aforementioned SNPs with an r.sup.2.gtoreq.0.80; the genotyping is
carried out by sequencing, selective hybridization, or selective
amplification; and the risk of developing autism is determined
based on the number of autism-associated risk alleles detected in
the biological sample.
16. The method of claim 15, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of CNTN6, NTRK3, DLG4, ERC2, TRIM9, SYT14, JARID2,
CDH13, SULF2, GRIN2A and NRG3, or combinations thereof.
17. The method of claim 15, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of APBA1, ABR, NRG3, PDE4D and EGLN3, or combinations
thereof.
18. The method of claim 15, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of RGS6, SLC24A2, PTPRD, NAV2, PCDH10, MAP1S, and
PAX2, or combinations thereof.
19. The method of claim 15, further comprising detecting
autism-associated risk alleles by genotyping a SNP in the gene loci
of any or all of EN2, JARID2, MARK1, ITGB3, and CNTNAP2, or
combinations thereof.
20. (canceled)
21. The method of claim 1, wherein the risk of developing autism is
determined depending on the number of autism-associated risk
alleles that are detected, by calculating a genetic score.
22. The method of claim 21, wherein the genetic score is compared
to one or more threshold values.
23. The method of claim 1, wherein the subject is not related to
anyone with an autism-spectrum disorder (ASD) or is a sibling of an
individual with an ASD.
24-30. (canceled)
31. A method for treating autism in a subject, the method
comprising: a) determining a risk of developing autism in a subject
by the method of claim 1, and b) if said subject is determined to
be at risk of developing autism, then submitting said subject to:
i) a behavioral autism instrument, ii) an indirect, interview-based
autism instrument with third parties, iii) Early Intensive
Behavioural Intervention, or iv) a combination of at least two of
i) to iii).
32. The method of claim 9, wherein the SNP is selected from the
group consisting of rs6872664, rs2278556, rs1861972, rs7766973,
rs12410279, rs5918, rs7794745, and rs10951154, or combinations
thereof.
33. The method of claim 11, wherein the SNP is selected from the
group consisting rs12514116, rs16916456, rs7172184, rs8123323,
rs10766739, rs16868972, rs12535987 and rs2076683, or combinations
thereof
34. The method of claim 12, wherein the SNP is selected from the
group consisting of rs723811, rs11942354, rs1597611, rs4404561 and
rs6962352, or combinations thereof.
35. The method of claim 13, wherein the SNP is selected from the
group consisting of rs2695112, rs6923644, rs1556060, rs1041644,
rs4782109, rs7225320, rs9307866, rs7021928, rs1569284, rs1432443,
rs220836, and rs7974275, or combinations thereof.
36. The method of claim 14, wherein the SNP is selected from the
group consisting of rs6872664, rs2278556, rs1861972, rs7766973,
rs7794745, and rs10951154, or combinations thereof.
37. The method of claim 16, wherein the SNP is selected from the
group consisting of rs9837484, rs7172184, rs314253, rs1485677,
rs10150121, rs7534723, rs9370809, rs9940922, rs6063144, rs4782109
and rs2820100, or combinations thereof.
38. The method of claim 17, wherein the SNP is selected from the
group consisting of rs11139294, rs2663327, rs7075400, rs35284 and
rs946630, or combinations thereof.
39. The method of claim 18, wherein the SNP is selected from the
group consisting of rs6574041, rs957910, rs2382104, rs10500866,
rs4404561, rs12985015, and rs2077642, or combinations thereof.
40. The method of claim 19, wherein the SNP is selected from the
group consisting of rs1861972, rs7766973, rs12410279, rs5918, and
rs7794745, or combinations thereof.
Description
[0001] The present invention relates to a method of determining a
risk of autism, or of detecting or the predisposition to or the
presence of autism in a subject by detecting a combination of risk
alleles in several genes simultaneously.
BACKGROUND OF THE INVENTION
[0002] The Pervasive Developmental disorders (PDDs) referred here
as "autism" are a heterogeneous group of disorders characterized by
impairments in social interaction, deficits in verbal and nonverbal
communication, restricted interests, and repetitive behaviors. The
disorders included in the spectrum are Pervasive Developmental
disorder, Not Otherwise Specified (PDD-NOS), Autistic disorder,
Childhood Disintegrative disorder, Asperger syndrome, and Rett
syndrome. Autism spectrum disorders (ASDs) represent three of the
PDDs: Autistic disorder (AUT), Asperger syndrome (AS), and
PDD-NOS.
[0003] The ASDs are currently diagnosed through clinical
evaluation. Two standardized instruments are considered as "gold
standards" in the diagnostic evaluation of autism: Autism
Diagnostic Observation Schedule-Generic [ADOS-G] (Gotham et al.
2007) and the Autism Diagnostic Interview-Revised [ADI-R]) (Lord et
al. 1994). The ADI-R is a semi-structured diagnostic interview
conducted with parents that allows quantitative exploration of
three domains altered in autism. It provides a diagnostic
assessment from the age of 36 months. Only recently, a revised
algorithm was published for young children aged 12-47 months (Kim
and Lord 2012). The ADOS is a scale of observation of the child. It
has been developed for children with language age equivalent of at
least 36 months. A version for children aged less than 30 months
with a mental age of at least 12 months has recently been
developed: the ADOS-Toddler Module (Luyster et al. 2009). Those
tools require training and are usually carried out by psychiatrists
or psychologists.
[0004] Several screening-tools have been validated to date (Barton
et al. 2011). Despite limited database regarding the psychometric
properties of specific screeners, their value of screening is
recognized. However, no specific screening tool for autism has been
validated in children less than 12 months. For example, the M-CHAT
(Robins et al., 2001), the most widely used, has been validated in
children aged from 18 to 24 months.
[0005] The prevalence of ASDs has been recently estimated to 1 per
110 children in the US (Rice et al, 2009), making autism one of the
most frequent childhood neuro developmental disorders, with males
being more likely to have a diagnosis than females (male to female
ratio of approximately 4:1). Autism has a strong genetic component,
and siblings of autistic children have an increased risk of disease
of approximately 19% (Ozonoff et al. 2011) compared to the
prevalence. Monozygotic and dizygotic twin studies have shown that
autism has a significant genetic component with monozygotic twin
concordance rates estimated between 70-80% (Hallmayer et al, 2011).
Autism does not follow a simple Mendelian inheritance pattern and
this is thought to be due to the involvement of multiple genes
(Veenstra-VanderWeele et al. 2004) with evidence for sex-specific
risk alleles in autism (Stone et al. 2004).
[0006] Spontaneous mutations or rare inherited variants may help to
explain etiology for a minority of cases, the inheritance pattern
of common variants is likely central to disease risk in a majority
of multiplex families.
[0007] There is no drug therapy available for ASDs, although some
autistic individuals have been treated with anti-depressant drugs
(e.g. fluoxetine) for secondary symptoms. The main treatments
proposed are based on intensive educational programs. Applied early
enough some studies show that as many as 50% of autistic children
participating in those programs can be referred back to normal
schooling and education. The age at which the therapy is proposed
is of significant importance. Ideally the programs should start at
18 months age. However, if early symptoms and parental concerns at
12 and 18 months may be predictive of ASD diagnostic (Zwaigenbaum,
2010), the literature suggests that children do not receive a
formal diagnosis of autism until the age of four (Shattuck et al.,
2009, Chamak et al., 2011, CDC, 2012).
[0008] Several genes or SNPs associated with ASDs have been
identified by academic groups and through in-house research efforts
at IntegraGen SA (IntegraGen). For instance, Hussman et al, 2011
describes several hundreds of candidate genes for association to
autism. Coutihno et al, 2007 analyzed the role in autism etiology
of seven candidate genes in the serotonin metabolic and
neurotransmission pathways and report a significant main effect of
HTR5A in autism. Voineagu et al, 2011 and Martin et al, 2007
describe the neuronal specific splicing factor A2BP1 (also known as
FOX1) as an autism susceptibility gene. Morrow et al, 2008
describes several known candidate genes associated to autism, as
well as new candidate genes associated to autism, including PCDH10,
DIA1 (c3orf58), NHE9 (SLC9A9), CNTN3, SCN7A and RNF8. Wang et al,
2009(a) describe 30 SNPs, located between genes CDH10 and CDH9 or
in or bear other genes, as associated to autism. Weiss et al, 2009
describes several SNPs associated to autism, and involves gene
SEMA5A as an autism susceptibility gene. Anney et al, 2010
discloses a SNP and 7 genes as associated to autism. WO2009/043178,
WO2011/031786, and US2011/0207124 describe association to autism of
various gene variants or SNPs. While these applications claim
methods for diagnosing autism or risk of autism, no data
demonstrating that a true diagnosis, with acceptable sensitivity,
specificity, and positive and negative predictive values may be
obtained by analyzing the disclosed gene variants or SNPs is
presented. Only individual associations of gene variants or SNPs
are described. Therefore, many genes or SNPs have been individually
described as associated to autism or risk of autism. However, the
contribution to disease risk of each individual gene identified is
generally low, and the odds ratio per risk allele rarely is above
1.5. Thus, the predictive power for each gene individually is too
small to be of clinical utility in complex diseases. In this
respect, Abrahams et al, 2008, Voineagu et al, 2012, and Scherer et
al, 2011 review the various methods for identifying genes or SNPs
associated to autism and clearly highlight both the presence of
genetic factors increasing the risk of autism and the high
heterogeneity and complexity of these factors.
[0009] Even if the risk of autism associated to each SNP remains
modest, the accumulation of multiple risk-associated alleles
markedly increases the risk to develop autism (Carayol et al, 2010
and Carayol et al, 2011) supporting a polygenic component in
autism. Such a polygenic model predicts that the more markers are
used, the better autism could be predicted with the use of genetic
scores that reflect the joint effect of multiple risk-associated
SNPs.
[0010] A first multiple biomarker-based tool combining analysis of
4 distinct SNPs in 4 distinct genes (PITX1, ATP2B2, SLC25A12, and
EN2) was developed and demonstrated to be able to estimate a
predictive value of the risk to develop autism in siblings with
unknown status of affected individuals (Carayol J et al, 2010 and
US2011/0086777). A second multiple biomarker-based tool combining
analysis of 8 distinct SNPs in 8 distinct genes, including 3 of
genes of the previous test (PITX1, ATP2B2, EN2, JARID2MARK1, ITGB3,
CNTNAP2, and HOXA1) was later developed and demonstrated to be able
to estimate a predictive value of the risk to develop autism in
siblings with unknown status of affected individuals (Carayol J et
al, 2011 and WO2011/138372).
[0011] The ARISk.RTM. Familial Autism Panel, proposed by
Transgenomic, Inc. in the USA, simultaneously tests eight SNPs in
eight independent genes which have been shown to be associated with
the development of autism.
[0012] However there is still a need for genetic tests with an
improved predictive power that could be easily applied at any age
and for pre-screening of individuals for eligibility for an ADI-R,
thereby substantially shortening the time from diagnosis to
treatment.
SUMMARY OF THE INVENTION
[0013] The invention relates to a method of determining a risk of
autism, or of detecting the predisposition to or the presence of
autism in a subject, the method comprising detecting the combined
presence of risk-associated single nucleotide polymorphism (SNP)
alleles at multiple loci in a sample from said subject. The
inventors have now identified a new set of genes, and more
particularly a new set of SNPs, useful in a genetic test for
determining whether an individual is at risk of autism.
[0014] The invention more particularly provides a method of
determining a risk of autism, or of detecting predisposition to or
the presence of autism in a subject, the method comprising
genotyping a SNP in the gene loci of at least HTR5A, MACF1, RBFOX1,
ABR, PTPRG, CACNA2D1, GFRA1, DSCAML1, CHRM3, LPPR4, DLG2, SLC9A9
and BASP1 in a sample from said subject.
[0015] In a particular embodiment, the method further comprises
genotyping a SNP in the gene loci of any or all of KCNIP1, UGCG,
NTRK3, PLCB1, NELL1, GPR98, MAGI2, PLAGL1, CNTN6, DLG4, ERC2,
TRIM9, SYT14, JARID2, CDH13, SULF2, GRIN2A and NRG3, or
combinations thereof.
[0016] In a another particular embodiment, the method further
comprises genotyping a SNP in the gene loci of any or all of NRG1,
TRIM2, EPHA5, PCDH10, HIP1, APBA1, PDE4D and EGLN3, or combinations
thereof.
[0017] In a preferred embodiment, the method further comprises the
additional genotyping of at least one SNP in the gene loci selected
from the group consisting of ABR, ACCN1, AKAP7, APBA1, ASTN2,
BASP1, CACNA2D1, CADM1, CDH13, CHRM3, CNTN6, DCLK1, DCLK2, DLG2,
DLG4, DSCAML1, EGLN3, EPHA5, ERC2, GFRA1, GPR98, GRIN2A, GRIN2B,
GRM7, HIP1, HTR5A, JARID2, KCNH5, KCNIP1, LPPR4, MACF1, MAGI2,
MAP1S, MAP2K1, NAV2, NELL1, NRG1, NRG3, NTRK3, PAX2, PCDH10,
PDE11A, PDE4D, PLAGL1, PLCB1, PTPRD, PTPRG, RBFOX1, RGS6, SLC24A2,
SLC9A9, SULF2, SYT14, TRIM2, TRIM9 and UGCG, or combinations
thereof.
[0018] In particular, the method may comprise genotyping of at
least one SNP in all of the following the gene loci: ABR, ACCN1,
AKAP7, APBA1, ASTN2, BASP1, CACNA2D1, CADM1, CDH13, CHRM3, CNTN6,
DCLK1, DCLK2, DLG2, DLG4, DSCAML1, EGLN3, EPHA5, ERC2, GFRA1,
GPR98, GRIN2A, GRIN2B, GRM7, HIP1, HTR5A, JARID2, KCNH5, KCNIP1,
LPPR4, MACF1, MAGI2, MAP1S, MAP2K1, NAV2, NELL1, NRG1, NRG3, NTRK3,
PAX2, PCDH10, PDE11A, PDE4D, PLAGL1, PLCB1, PTPRD, PTPRG, RBFOX1,
RGS6, SLC24A2, SLC9A9, SULF2, SYT14, TRIM2, TRIM9 and UGCG.
[0019] In the above methods, preferably, the SNP in HTR5A is
rs893109 (position 27 of SEQ ID NO: 31), MACF1 is rs260969
(position 27 of SEQ ID NO: 15), RBFOX1 is rs12925135 (position 27
of SEQ ID NO: 39), ABR is rs2663327 (position 27 of SEQ ID NO: 40),
PTPRG is rs636624 (position 27 of SEQ ID NO: 22), CACNA2D1 is
rs2367910 (position 27 of SEQ ID NO: 41), GFRA1 is rs10787637
(position 27 of SEQ ID NO: 4), DSCAML1 is rs695083 (position 27 of
SEQ ID NO: 24), CHRM3 is rs10802802 (position 27 of SEQ ID NO: 5),
LPPR4 is rs712886 (position 27 of SEQ ID NO: 27), DLG2 is
rs12275631 (position 27 of SEQ ID NO: 51), SLC9A9 is rs3928471
(position 27 of SEQ ID NO: 19), BASP1 is rs298542 (position 27 of
SEQ ID NO: 52), KCNIP1 is rs12514116 (position 27 of SEQ ID NO:
38), UGCG is rs16916456 (position 27 of SEQ ID NO: 11), NTRK3 is
rs7172184 (position 27 of SEQ ID NO: 28), PLCB1 is rs8123323
(position 27 of SEQ ID NO: 37), NELL1 is rs10766739 (position 27 of
SEQ ID NO: 3), GPR98 is rs16868972 (position 27 of SEQ ID NO: 42),
MAGI2 is rs12535987 (position 27 of SEQ ID NO: 43), PLAGL1 is
rs2076683 (position 27 of SEQ ID NO: 12), CNTN6 is rs9837484
(position 27 of SEQ ID NO: 35), DLG4 is rs314253 (position 27 of
SEQ ID NO: 17), ERC2 is rs1485677 (position 27 of SEQ ID NO: 8),
TRIM9 is rs10150121 (position 27 of SEQ ID NO: 1), SYT14 is
rs7534723 (position 27 of SEQ ID NO: 30), JARID2 is rs9370809
(position 27 of SEQ ID NO: 33), CDH13 is rs9940922 (position 27 of
SEQ ID NO: 36), SULF2 is rs6063144 (position 27 of SEQ ID NO: 53),
GRIN2A is rs4782109 (position 27 of SEQ ID NO: 21), NRG3 is
rs2820100 (position 27 of SEQ ID NO: 54) or rs7075400 (position 27
of SEQ ID NO: 55), NRG1 rs723811 (position 27 of SEQ ID NO: 44),
TRIM2 is rs11942354 (position 27 of SEQ ID NO: 45), EPHA5 is
rs1597611 (position 27 of SEQ ID NO: 10), PCDH10 is rs4404561
(position 27 of SEQ ID NO: 20), HIP1 is rs6962352 (position 27 of
SEQ ID NO: 25), APBA1 is rs11139294 (position 27 of SEQ ID NO: 6),
PDE4D is rs35284 (position 27 of SEQ ID NO: 18), EGLN3 is rs946630
(position 27 of SEQ ID NO: 56), KCNH5 is rs1041644 (position 27 of
SEQ ID NO: 2), MAP1S is rs12985015 (position 27 of SEQ ID NO: 7),
GRM7 is rs1569284 (position 27 of SEQ ID NO: 9), PAX2 is rs2077642
(position 27 of SEQ ID NO: 13), PTPRD is rs2382104 (position 27 of
SEQ ID NO: 14), PDE11A is rs2695112 (position 27 of SEQ ID NO: 16),
RGS6 is rs6574041 (position 27 of SEQ ID NO: 23), ASTN2 is
rs7021928 (position 27 of SEQ ID NO: 26), ACCN1 is rs7225320
(position 27 of SEQ ID NO: 29), DCLK2 is rs9307866 (position 27 of
SEQ ID NO: 32), SLC24A2 is rs957910 (position 27 of SEQ ID NO: 34),
AKAP7 is rs6923644 (position 27 of SEQ ID NO: 46), DCLK1 is
rs1556060 (position 27 of SEQ ID NO: 47), MAP2K1 is rs1432443
(position 27 of SEQ ID NO: 48), CADM1 is rs220836 (position 27 of
SEQ ID NO: 49), GRIN2B is rs7974275 (position 27 of SEQ ID NO: 50)
and/or NAV2 is rs10500866 (position 27 of SEQ ID NO: 57). Most
preferably, all SNPs genotyped are those mentioned in previous
sentence.
[0020] The invention thus in particular provides a method of
determining a risk of autism, or of detecting the predisposition to
or presence of autism in a subject, the method comprising
genotyping of SNPs in a sample from said subject, wherein said SNPs
are rs2663327, rs7225320, rs6923644, rs11139294, rs7021928,
rs298542, rs2367910, rs220836, rs9940922, rs10802802, rs9837484,
rs1556060, rs9307866, rs12275631, rs314253, rs695083, rs946630,
rs1597611, rs1485677, rs10787637, rs16868972, rs4782109, rs7974275,
rs1569284, rs6962352, rs893109, rs9370809, rs1041644, rs12514116,
rs712886, rs260969, rs12535987, rs12985015, rs1432443, rs10500866,
rs10766739, rs723811, rs2820100, rs7075400, rs7172184, rs2077642,
rs4404561, rs2695112, rs35284, rs2076683, rs8123323, rs2382104,
rs636624, rs12925135, rs6574041, rs957910, rs3928471, rs6063144,
rs7534723, rs11942354, rs10150121, rs16916456.
[0021] The method may also further comprise genotyping a SNP in the
gene loci of any or all of PITX1, ATP2B2, EN2, JARID2, MARK1,
ITGB3, CNTNAP2, and HOXA1, or combinations thereof, preferably the
method further comprises genotyping any or all of the SNP selected
from the group consisting rs6872664, rs2278556, rs1861972,
rs7766973, rs12410279, rs5918, rs7794745, and rs10951154, or
combinations thereof.
[0022] The invention further provides a method of determining a
risk of autism, or of detecting the predisposition or presence of
autism in a male subject, the method comprising genotyping a SNP in
the gene loci of at least HTR5A, MACF1, RBFOX1, ABR, PTPRG, and
CACNA2D1, in a sample from said subject. Preferably, the SNP in
HTR5A is rs893109 (position 27 of SEQ ID NO: 31), in MACF1 is
rs260969 (position 27 of SEQ ID NO: 15), in RBFOX1 is rs12925135
(position 27 of SEQ ID NO: 39), in ABR is rs2663327 (position 27 of
SEQ ID NO: 40), in PTPRG is rs636624 (position 27 of SEQ ID NO:
22), and/or in CACNA2D1 is rs2367910 (position 27 of SEQ ID NO:
41). Most preferably, all SNPs genotyped are those mentioned in
previous sentence. The invention thus in particular provides a
method of determining a risk of autism, or of detecting the
predisposition or presence of autism in a male subject, the method
comprising genotyping of SNPs in a sample from said subject,
wherein said SNPs are rs893109, rs260969, rs12925135, rs2663327,
rs636624 and rs2367910.
[0023] Preferably, the method further comprises genotyping a SNP in
the gene loci of any or all of KCNIP1, UGCG, NTRK3, PLCB1, NELL1,
GPR98, MAGI2, and PLAGL1, or combinations thereof. In this case,
advantageously, the SNP in KCNIP1 is rs12514116 (position 27 of SEQ
ID NO: 38), in UGCG is rs16916456 (position 27 of SEQ ID NO: 11),
in NTRK3 is rs7172184 (position 27 of SEQ ID NO: 28), in PLCB1 is
rs8123323 (position 27 of SEQ ID NO: 37), in NELL1 is rs10766739
(position 27 of SEQ ID NO: 3), in GPR98 is rs16868972 (position 27
of SEQ ID NO: 42), in MAGI2 is rs12535987 (position 27 of SEQ ID
NO: 43), and/or in PLAGL1 is rs2076683 (position 27 of SEQ ID NO:
12). Most preferably, all SNPs genotyped are those mentioned in
previous sentence. Thus, preferably, the method further comprises
genotyping any or all of the SNP selected from the group consisting
rs12514116, rs16916456, rs7172184, rs8123323, rs10766739,
rs16868972, rs12535987 and rs2076683, or combinations thereof.
[0024] More preferably, the method further comprises genotyping a
SNP in the gene loci of any or all of NRG1, TRIM2, EPHA5, PCDH10,
and HIP1, or combinations thereof. In this case, advantageously,
the SNP in NRG1 is rs723811 (position 27 of SEQ ID NO: 44), in
TRIM2 is rs11942354 (position 27 of SEQ ID NO: 45), in EPHA5 is
rs1597611 (position 27 of SEQ ID NO: 10), in PCDH10 is rs4404561
(position 27 of SEQ ID NO: 20), and/or in HIP1 is rs6962352
(position 27 of SEQ ID NO: 25). Most preferably, all SNPs genotyped
are those mentioned in previous sentence. Thus, the method
preferably further comprises genotyping any or all of the SNP
selected from the group consisting of rs723811, rs11942354,
rs1597611, rs4404561 and rs6962352, or combinations thereof.
[0025] Even more preferably, the method further comprises
genotyping a SNP in the gene loci of any or all of PDE11A, AKAP7,
DCLK1, KCNH5, GRIN2A, ACCN1, DCLK2, ASTN2, GRM7, MAP2K1, CADM1, and
GRIN2B, or combinations thereof. In this case, advantageously, the
SNP in PDE11A is rs2695112 (position 27 of SEQ ID NO: 16), in AKAP7
is rs6923644 (position 27 of SEQ ID NO: 46), near 3' of DCLK1 is
rs1556060 (position 27 of SEQ ID NO: 47), in KCNH5 is rs1041644
(position 27 of SEQ ID NO: 2), in GRIN2A is rs4782109 (position 27
of SEQ ID NO: 21), in ACCN1 is rs7225320 (position 27 of SEQ ID NO:
29), in DCLK2 is rs9307866 (position 27 of SEQ ID NO: 32), in ASTN2
is rs7021928 (position 27 of SEQ ID NO: 26), in GRM7 is rs1569284
(position 27 of SEQ ID NO: 9), in MAP2K1 is rs1432443 (position 27
of SEQ ID NO: 48), in CADM1 is rs220836 (position 27 of SEQ ID NO:
49), and/or in GRIN2B is rs7974275 (position 27 of SEQ ID NO: 50).
Most preferably, all SNPs genotyped are those mentioned in previous
sentence. Thus, the method preferably further comprises genotyping
any or all of the SNP selected from the group consisting of
rs2695112, rs6923644, rs1556060, rs1041644, rs4782109, rs7225320,
rs9307866, rs7021928, rs1569284, rs1432443, rs220836, and
rs7974275, or combinations thereof.
[0026] In a preferred embodiment, the method further provides a
method of determining a risk of autism, or of detecting the
predisposition or presence of autism in a male subject, the method
comprising genotyping any SNP or any combination of SPNs as
identified in Table 1 or in Table 5.
[0027] The method may also further comprise genotyping a SNP in the
gene loci of any or all of PITX1, ATP2B2, EN2, JARID2, CNTNAP2, and
HOXA1, or combinations thereof, preferably the method further
comprises genotyping any or all of the SNP selected from the group
consisting rs6872664, rs2278556, rs1861972, rs7766973, rs7794745,
and rs10951154, or combinations thereof.
[0028] The invention further provides a method of determining a
risk of autism, or of detecting the predisposition or presence of
autism in a female subject, the method comprising genotyping a SNP
in the gene loci of at least CHRM3, DSCAML1, PTPRG, GFRA1, LPPR4,
DLG2, SLC9A9 and BASP1, in a sample from said subject. Preferably,
the SNP in CHRM3 is rs10802802 (position 27 of SEQ ID NO: 5), in
DSCAML1 is rs695083 (position 27 of SEQ ID NO: 24), in PTPRG is
rs636624 (position 27 of SEQ ID NO: 22), in LPPR4 is rs712886
(position 27 of SEQ ID NO: 27), in DLG2 is rs12275631 (position 27
of SEQ ID NO: 51), in SLC9A9 is rs3928471 (position 27 of SEQ ID
NO: 19), in BASP1 is rs298542 (position 27 of SEQ ID NO: 52). Most
preferably, all SNPs genotyped are those mentioned in previous
sentence. The invention thus further provides a method of
determining a risk of autism, or of detecting the predisposition to
or presence of autism in a female subject, the method comprising
genotyping of SNPs in a sample from said subject, wherein said SNPs
are rs10802802, rs695083, rs636624, rs10787637, rs712886,
rs12275631, rs3928471 and rs298542.
[0029] Preferably, the method further comprises genotyping a SNP in
the gene loci of any or all of CNTN6, NTRK3, DLG4, ERC2, TRIM9,
SYT14, JARID2, CDH13, SULF2, GRIN2A and NRG3, or combinations
thereof. In this case, advantageously, the SNP in CNTN6 is
rs9837484 (position 27 of SEQ ID NO: 35), in NTRK3 is rs7172184
(position 27 of SEQ ID NO: 28), in DLG4 is rs314253 (position 27 of
SEQ ID NO: 17), in ERC2 is rs1485677 (position 27 of SEQ ID NO: 8),
in TRIM9 is rs10150121 (position 27 of SEQ ID NO: 1), in SYT14 is
rs7534723 (position 27 of SEQ ID NO: 30), in JARID2 is rs9370809
(position 27 of SEQ ID NO: 33), in CDH13 is rs9940922 (position 27
of SEQ ID NO: 36), in SULF2 is rs6063144 (position 27 of SEQ ID NO:
53), in GRIN2A is rs4782109 (position 27 of SEQ ID NO: 21), and/or
in NRG3 is rs2820100 (position 27 of SEQ ID NO: 54) or rs7075400
(position 27 of SEQ ID NO: 55). Most preferably, all SNPs genotyped
are those mentioned in previous sentence. Thus, preferably, the
method further comprises genotyping any or all of the SNP selected
from the group consisting of rs9837484, rs7172184, rs314253,
rs1485677, rs10150121, rs7534723, rs9370809, rs9940922, rs6063144,
rs4782109 and rs2820100, or combinations thereof.
[0030] More preferably, the method further comprises genotyping a
SNP in the gene loci of any or all of APBA1, ABR, NRG3, PDE4D and
EGLN3, or combinations thereof. In this case, advantageously, the
SNP in APBA1 is rs11139294 (position 27 of SEQ ID NO: 6), in ABR is
rs2663327 (position 27 of SEQ ID NO: 40), in NRG3 is rs7075400
(position 27 of SEQ ID NO: 55), in PDE4D is rs35284 (position 27 of
SEQ ID NO: 18), and/or in EGLN3 is rs946630 (position 27 of SEQ ID
NO: 56). Most preferably, all SNPs genotyped are those mentioned in
previous sentence. Thus, the method preferably further comprises
genotyping any or all of the SNP selected from the group consisting
of rs11139294, rs2663327, rs7075400, rs35284 and rs946630, or
combinations thereof.
[0031] Even more preferably, the method further comprises
genotyping a SNP in the gene loci of any or all of RGS6, SLC24A2,
PTPRD, NAV2, PCDH10, MAP1S, and PAX2, or combinations thereof. In
this case, advantageously, the SNP in RGS6 is rs6574041 (position
27 of SEQ ID NO: 23), in SLC24A2 is rs957910 (position 27 of SEQ ID
NO: 34), in PTPRD is rs2382104 (position 27 of SEQ ID NO: 14), in
NAV2 is rs10500866 (position 27 of SEQ ID NO: 57), in PCDH10 is
rs4404561 (position 27 of SEQ ID NO: 20), in MAP1S is rs12985015
(position 27 of SEQ ID NO: 7), and/or in PAX2 is rs2077642
(position 27 of SEQ ID NO: 13). Most preferably, all SNPs genotyped
are those mentioned in previous sentence. Thus, the method
preferably further comprises genotyping any or all of the SNP
selected from the group consisting of rs6574041, rs957910,
rs2382104, rs10500866, rs4404561, rs12985015, and rs2077642, or
combinations thereof.
[0032] In a preferred embodiment, the method further provides a
method of determining a risk of autism, or of detecting the
predisposition or presence of autism in a female subject, the
method comprising genotyping any SNP as identified in Table 1 or in
Table 6.
[0033] The method may also further comprise genotyping a SNP in the
gene loci of any or all of EN2, JARID2, MARK1, ITGB3, and CNTNAP2,
or combinations thereof, preferably the method further comprises
genotyping any or all of the SNP selected from the group consisting
rs1861972, rs7766973, rs12410279, rs5918, and rs7794745, or
combinations thereof.
[0034] In the methods of the invention, detecting the combined
presence of risk-associated alleles, preferably as defined in Table
1, is indicative of a risk of autism, a predisposition to autism,
or presence of autism in a subject. The level of risk or the
likelihood of predisposition or presence of autism is determined
depending on the number of risk-associated alleles that are
detected, preferably by calculating a genetic score, as described
in the Experimental section.
[0035] In one embodiment, the method of the invention comprises, or
further comprises, genotyping any SNP in linkage disequilibrium
with any of the SNP identified above, wherein said SNP in linkage
disequilibrium is within the gene of said SNP identified above. In
particular, the presence of SNPs in linkage disequilibrium (LD)
with the above identified SNPs may be genotyped, in place of, or in
addition to, said identified SNPs. In the context of the present
invention, the SNPs in linkage disequilibrium with the above
identified SNP are within the same gene of the above identified
SNP.
[0036] The invention further provides a kit comprising primers
pairs (forward and reverse primers) or triplets (two forward and
one reverse primers) and/or probes for the specific detection of a
SNP in the gene loci of at least HTR5A, MACF1, RBFOX1, ABR, PTPRG,
CACNA2D1, GFRA1, DSCAML1, CHRM3, LPPR4, DLG2, SLC9A9 and BASP1,
preferably the SNPs are rs893109 in HTR5A (position 27 of SEQ ID
NO: 31), rs260969 in MACF1 (position 27 on SEQ ID NO: 15),
rs12925135 in RBFOX1 (position 27 of SEQ ID NO: 39), rs2663327 in
ABR (position 27 of SEQ ID NO: 40), rs636624 in PTPRG (position 27
of SEQ ID NO: 22), rs2367910 in CACNA2D1 (position 27 of SEQ ID NO:
41), rs10787637 in GFRA1 (position 27 of SEQ ID NO: 4), rs695083 in
DSCAML1 (position 27 of SEQ ID NO: 24), rs10802802 in CHRM3
(position 27 of SEQ ID NO: 5), rs712886 in LPPR4 (position 27 of
SEQ ID NO: 27), rs12275631 in DLG2 (position 27 of SEQ ID NO: 51),
rs3928471 in SLC9A9 (position 27 of SEQ ID NO: 19), and rs298542 in
BASP1 (position 27 of SEQ ID NO: 52).
[0037] The kit may further comprise primers pairs (forward and
reverse primers) or triplets (two forward and one reverse primers)
and/or probes for the specific detection of a SNP in the gene loci
of any or all of KCNIP1, UGCG, NTRK3, PLCB1, NELL1, GPR98, MAGI2,
PLAGL1, CNTN6, DLG4, ERC2, TRIM9, SYT14, JARID2, CDH13, SULF2,
GRIN2A and NRG3, or combinations thereof, preferably the kit
further comprises primers pairs (forward and reverse primers) or
triplets (two forward and one reverse primers) and/or probes for
the specific detection of any or all of rs12514116 in KCNIP1
(position 27 on SEQ ID NO: 38), rs16916456 in UGCG (position 27 of
SEQ ID NO: 11), rs7172184 in NTRK3 (position 27 of SEQ ID NO: 28),
rs8123323 in PLCB1 (position 27 of SEQ ID NO: 37), rs10766739 in
NELL1 (position 27 of SEQ ID NO: 3), rs16868972 in GPR98 position
27 of SEQ ID NO: 42), rs12535987 in MAGI2 (position 27 of SEQ ID
NO: 43), rs207668 in PLAGL1 (position 27 of SEQ ID NO: 12),
rs9837484 in CNTN6 (position 27 of SEQ ID NO: 35), rs314253 in DLG4
(position 27 of SEQ ID NO: 17), rs1485677 in ERC2 (position 27 of
SEQ ID NO: 8), rs10150121 in TRIM9 (position 27 of SEQ ID NO: 1),
rs7534723 in SYT14 (position 27 of SEQ ID NO: 30), rs9370809 in
JARID2 (position 27 of SEQ ID NO: 33), rs9940922 in CDH13 (position
27 of SEQ ID NO: 36), rs6063144 in SULF2 (position 27 of SEQ ID NO:
53), rs4782109 in GRIN2A (position 27 of SEQ ID NO: 21), and
rs2820100 in NRG3 (position 27 of SEQ ID NO: 54), or combinations
thereof.
[0038] Said kit may also or in addition further comprises primers
pairs (forward and reverse primers) or triplets (two forward and
one reverse primers) and/or probes for the specific detection of a
SNP in the gene loci of any or all of NRG1, TRIM2, EPHA5, PCDH10,
HIP1, APBA1, PDE4D and EGLN3, or combinations thereof, preferably
the kit further comprises primers pairs (forward and reverse
primers) or triplets (two forward and one reverse primers) and/or
probes for the specific detection of any or all of rs723811 in NRG1
(position 27 on SEQ ID NO: 44), rs11139294 in APBA1 (position 27 of
SEQ ID NO: 6), rs11942354 in TRIM2 (position 27 of SEQ ID NO: 45),
rs1597611 in EPHA5 (position 27 of SEQ ID NO: 10), rs4404561 in
PCDH10 (position 27 of SEQ ID NO: 20), rs6962352 in HIP1 (position
27 of SEQ ID NO: 25), rs7075400 in NRG3 (position 27 of SEQ ID NO:
55), rs35284 in PDE4D (position 27 of SEQ ID NO: 18) and rs946630
in EGLN3 (position 27 of SEQ ID NO: 56), or combinations
thereof.
[0039] Said kit may also or in addition further comprises primers
pairs (forward and reverse primers) or triplets (two forward and
one reverse primers) and/or probes for the specific detection of at
least one SNP in the gene loci selected from the group consisting
of ABR, ACCN1, AKAP7, APBA1, ASTN2, BASP1, CACNA2D1, CADM1, CDH13,
CHRM3, CNTN6, DCLK1, DCLK2, DLG2, DLG4, DSCAML1, EGLN3, EPHA5,
ERC2, GFRA1, GPR98, GRIN2A, GRIN2B, GRM7, HIP1, HTR5A, JARID2,
KCNH5, KCNIP1, LPPR4, MACF1, MAGI2, MAP1S, MAP2K1, NAV2, NELL1,
NRG1, NRG3, NTRK3, PAX2, PCDH10, PDE11A, PDE4D, PLAGL1, PLCB1,
PTPRD, PTPRG, RBFOX1, RGS6, SLC24A2, SLC9A9, SULF2, SYT14, TRIM2,
TRIM9 and UGCG, or combinations thereof, preferably the kit further
comprises primers pairs (forward and reverse primers) or triplets
(two forward and one reverse primers) and/or probes for the
specific detection of any or all of rs2663327, rs7225320,
rs6923644, rs11139294, rs7021928, rs298542, rs2367910, rs220836,
rs9940922, rs10802802, rs9837484, rs1556060, rs9307866, rs12275631,
rs314253, rs695083, rs946630, rs1597611, rs1485677, rs10787637,
rs16868972, rs4782109, rs7974275, rs1569284, rs6962352, rs893109,
rs9370809, rs1041644, rs12514116, rs712886, rs260969, rs12535987,
rs12985015, rs1432443, rs10500866, rs10766739, rs723811, rs2820100,
rs7075400, rs7172184, rs2077642, rs4404561, rs2695112, rs35284,
rs2076683, rs8123323, rs2382104, rs636624, rs12925135, rs6574041,
rs957910, rs3928471, rs6063144, rs7534723, rs11942354, rs10150121,
rs16916456, or combinations thereof.
[0040] In particular, the kit may comprise primers pairs (forward
and reverse primers) or triplets (two forward and one reverse
primers) and/or probes for the specific detection of at least one
SNP in all of the following the gene loci: ABR, ACCN1, AKAP7,
APBA1, ASTN2, BASP1, CACNA2D1, CADM1, CDH13, CHRM3, CNTN6, DCLK1,
DCLK2, DLG2, DLG4, DSCAML1, EGLN3, EPHA5, ERC2, GFRA1, GPR98,
GRIN2A, GRIN2B, GRM7, HIP1, HTR5A, JARID2, KCNH5, KCNIP1, LPPR4,
MACF1, MAGI2, MAP1S, MAP2K1, NAV2, NELL1, NRG1, NRG3, NTRK3, PAX2,
PCDH10, PDE11A, PDE4D, PLAGL1, PLCB1, PTPRD, PTPRG, RBFOX1, RGS6,
SLC24A2, SLC9A9, SULF2, SYT14, TRIM2, TRIM9 and UGCG, preferably
the kit comprises primers pairs (forward and reverse primers) or
triplets (two forward and one reverse primers) and/or probes for
the specific detection of all following SNPs: rs2663327, rs7225320,
rs6923644, rs11139294, rs7021928, rs298542, rs2367910, rs220836,
rs9940922, rs10802802, rs9837484, rs1556060, rs9307866, rs12275631,
rs314253, rs695083, rs946630, rs1597611, rs1485677, rs10787637,
rs16868972, rs4782109, rs7974275, rs1569284, rs6962352, rs893109,
rs9370809, rs1041644, rs12514116, rs712886, rs260969, rs12535987,
rs12985015, rs1432443, rs10500866, rs10766739, rs723811, rs2820100,
rs7075400, rs7172184, rs2077642, rs4404561, rs2695112, rs35284,
rs2076683, rs8123323, rs2382104, rs636624, rs12925135, rs6574041,
rs957910, rs3928471, rs6063144, rs7534723, rs11942354, rs10150121,
rs16916456.
[0041] The kit may also further comprise primers pairs (forward and
reverse primers) or triplets (two forward and one reverse primers)
and/or probes for the specific detection of a SNP in the gene loci
of any or all of PITX1, ATP2B2, EN2, JARID2, MARK1, ITGB3, CNTNAP2,
and HOXA1, or combinations thereof, preferably the kit further
comprises primers pairs (forward and reverse primers) or triplets
(two forward and one reverse primers) and/or probes for the
specific detection of any or all of the SNP selected from the group
consisting rs6872664, rs2278556, rs1861972, rs7766973, rs12410279,
rs5918, rs7794745, and rs10951154, or combinations thereof.
[0042] Primer pairs (forward and reverse primers) or triplets (two
forward and one reverse primers) may be used for specific
amplification of part of a target gene comprising the SNP of
interest. When only two primers are used, they are generally
located each on one side of the target SNP of interest and are used
in order to increase the amount of target sequence for further
analysis. When three primers are used, the single reverse primer is
preferably located on one side of the target SNP of interest, while
the two corresponding forward primers are respectively specific of
the protective or risk-associated allele of the SNP. The base
differing between the two primers is preferably located in 3' of
the forward primers. Primers are polynucleotides of about 15 to
about 25 nucleotides, preferably of about 18 to about 22
nucleotides.
[0043] A probe for the specific detection of a SNP in a gene locus
may notably comprise or consist of a polynucleotide comprising at
least 10 contiguous bases, preferably about 10 to about 60 bases,
complementary to part of a target gene comprising the SNP of
interest.
[0044] In particular, the invention provides a set of
polynucleotides comprising at least 10 contiguous bases, preferably
about 10 to about 60 bases, of (i) SEQ ID NO: 31, 15, 39, 40, 22,
41, 4, 24, 5, 27, 51, 19 and 52 respectively around position 27 of
SEQ ID NO: 31, position 27 of SEQ ID NO:15, position 27 of SEQ ID
NO: 39, position 27 of SEQ ID NO: 40, position 27 of SEQ ID NO: 22,
position 27 of SEQ ID NO: 41, position 27 of SEQ ID NO: 4, position
27 of SEQ ID NO: 24, position 27 of SEQ ID NO:5, position 27 of SEQ
ID NO: 27, position 27 of SEQ ID NO: 51 and position 27 of SEQ ID
NO: 19, or (ii) of the complement of said sequences. Such a set of
polynucleotides may further comprise polynucleotides comprising at
least 10 contiguous bases, preferably about 10 to about 60 bases,
of (i) SEQ ID NO:38, SEQ ID NO:11, SEQ ID NO:28, SEQ ID NO:37, SEQ
ID NO:3, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:12, SEQ ID NO:35,
SEQ ID NO:17, SEQ ID NO:8, SEQ ID NO:1, SEQ ID NO:30, SEQ ID NO:33,
SEQ ID NO:36, SEQ ID NO:53, SEQ ID NO:21, SEQ ID NO:54 and SEQ ID
NO:55, respectively around positions of SEQ ID NO:38, SEQ ID NO:11,
SEQ ID NO:28, SEQ ID NO:37, SEQ ID NO:3, SEQ ID NO:42, SEQ ID
NO:43, SEQ ID NO:12, SEQ ID NO:35, SEQ ID NO:17, SEQ ID NO:8, SEQ
ID NO:1, SEQ ID NO:30, SEQ ID NO:33, SEQ ID NO:36, SEQ ID NO:53,
SEQ ID NO:21, SEQ ID NO:54 and SEQ ID NO:55 mentioned in Table 1,
or (ii) of the complement of said sequences. Such a set of
polynucleotides may further comprise polynucleotides comprising at
least 10 contiguous bases, preferably about 10 to about 60 bases,
of (i) SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:10, SEQ ID NO:20, SEQ
ID NO:25, SEQ ID NO:6, SEQ ID NO:18 and SEQ ID NO:56, respectively
around positions of SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:10, SEQ
ID NO:20, SEQ ID NO:25, SEQ ID NO:6, SEQ ID NO:18 and SEQ ID NO:56
mentioned in Table 1, or (ii) of the complement of said
sequences.
[0045] In a preferred embodiment, the invention provides a set of
polynucleotides comprising at least 10 contiguous bases, preferably
about 10 to about 60 bases, of (i) each of SEQ ID NO:1 to SEQ ID
NO:57, respectively around positions of SEQ ID NO:1 to SEQ ID NO:57
mentioned in Table 1, or (ii) of the complement of said
sequences.
[0046] The above sets of polynucleotides may further comprise at
least 10 contiguous bases, preferably about 10 to about 60 bases,
of (i) each of SEQ ID NO:58 to SEQ ID NO:65, respectively around
positions of SEQ ID NO:58 to SEQ ID NO:65 mentioned in Table 2, or
(ii) of the complement of said sequences.
[0047] A further subject of the invention is a microarray
comprising a set of polynucleotides and optionally, a substrate on
which the set of polynucleotides is immobilized, wherein the set of
polynucleotides is as defined above.
[0048] The inventors showed that the predictive value that is
obtained by detecting combinations of risk-associated alleles of
polymorphisms in these genes is superior to the predictive value
obtained when considering these risk-associated alleles
individually, demonstrating its clinical validity. Genotyping these
SNPs thus allows the estimation of a predictive value for the risk
of developing ASDs, not only in yet non-diagnosed siblings of
affected individuals, but more generally to any individual, in
particular any child.
[0049] The clinical utility of this test resides in its ability to
select at risk individuals for earlier down-stream diagnosis using
psychological profiling tests (e.g. ADI-R or ADOS). The test may
also be used in affected individuals to accompany these profiling
tests to substantiate the diagnosis for ASDs and distinguish it
from other psychiatric conditions.
[0050] The present invention further relates to methods for
treating or preventing autism in a subject, the method comprising:
[0051] a) determining a risk of autism, or detecting predisposition
to or the presence of autism in a subject by any method according
to the invention described herein, and [0052] b) if said subject is
determined to be at risk of autism, as predisposed to autism or as
suffering from autism, then submitting said subject to: [0053] i) a
behavioral autism instrument, such as Autism Diagnostic Observation
Schedule-Generic [ADOS-G], [0054] ii) an indirect, interview-based
autism instrument with third parties, such as Autism Diagnostic
Interview-Revised [ADI-R], and/or [0055] iii) Early Intensive
Behavioural Intervention (EIBI).
[0056] Preferably, if the subject is determined to be at risk of
autism, as predisposed to autism or as suffering from autism, then
said subject is first rapidly submitted to a behavioral or an
indirect, interview-based autism test, preferably the Autism
Diagnostic Interview-Revised [ADI-R] test in order to confirm the
diagnosis of autism. If autism diagnosis is confirmed, then the
subject is rapidly submitted to Early Intensive Behavioural
Intervention (EIBI), since early intervention has been found to
improve outcome for autistic subjects.
DETAILED DESCRIPTION OF THE INVENTION
[0057] Unless otherwise specified, the term "autism" refers to
Autism Spectrum Disorders (ASDs) which is a heterogeneous group of
disorders characterized by impairments in social interaction,
deficits in verbal and nonverbal communication, and restricted
repetitive and stereotyped patterns of behavior, interests and
activities. Autism Spectrum Disorders (ASDs) are preferably
targeted, they include the typical form of autism, Autistic
disorder (AUT), and forms differing by the age of beginning, the
number and the distribution of the autistic key symptoms, such as
Asperger syndrome (AS), childhood disintegrative disorder and
PDD-NOS. The methods of the invention are more preferably intended
for Autistic disorder (AUT).
[0058] The invention provides diagnostic screening methods based on
a monitoring of several genes in a subject. The subject may be at
early, pre-symptomatic stage, or late stage. The subject may be any
human male or female, preferably a child or a young adult. The
subject can be asymptomatic. The subject can have a family history
of autism or not. The method of the invention is useful when the
subject is a sibling of an individual with an autism-spectrum
disorder, i.e. an individual already diagnosed with an autism
spectrum disorder. However it may also be useful when the subject
to test is not related to anyone with an autism-spectrum
disorder.
[0059] The method of the invention can be performed at any age
after birth and used to pre-screen individuals requiring further
assessment with the ADI-R, shortening the time from diagnosis to
intervention.
[0060] The diagnosis methods can be performed in vitro, ex vivo or
in vivo, preferably in vitro or ex vivo. They use a sample from the
subject. The sample may be any biological sample derived from a
subject, which contains nucleic acids. Examples of such samples
include fluids, tissues, cell samples, organs, biopsies, etc. Most
preferred samples are blood, plasma, saliva, jugal cells, urine,
seminal fluid, etc. A particularly preferred sample is saliva. The
sample may be collected according to conventional techniques and
used directly for diagnosis or stored. The sample may be treated
prior to performing the method, in order to render or improve
availability of nucleic acids or polypeptides for testing.
Treatments include, for instance, lysis (e.g., mechanical,
physical, chemical, etc.), centrifugation, etc. Also, the nucleic
acids may be pre-purified or enriched by conventional techniques,
and/or reduced in complexity. Nucleic acids may also be treated
with enzymes or other chemical or physical treatments to produce
fragments thereof. Considering the high sensitivity of the claimed
methods, very small amounts of sample are sufficient to perform the
assay.
[0061] The finding of a specific allele in the sample is indicative
of the presence of a gene locus variant in the subject, which can
be correlated to the presence, predisposition or stage of
progression of autism. For example, an individual having a germ
line mutation has an increased risk of developing autism. The
determination of the presence of an altered gene locus in a subject
also allows the design of appropriate therapeutic intervention,
which is more effective and customized. Also, this determination at
the pre-symptomatic level allows a preventive regimen to be
applied.
Risk-Associated Genes and SNP
[0062] The invention relates to a method of determining a risk of
autism, or of detecting the predisposition to or the presence of
autism in a subject, the method comprising detecting the combined
presence of risk-associated single nucleotide polymorphism (SNP)
alleles at multiple loci in a sample from said subject. The
inventors have now identified a new set of genes, and more
particularly a new set of SNPs, useful in a genetic test for
determining whether an individual is at risk of autism. More
specifically, the inventors showed that specific combinations of
risk-associated alleles of selected SNPs allowed to obtain a
predictive power that is clinically very useful for determining a
risk of autism.
[0063] The invention more particularly provides a method of
determining a risk of autism, or of detecting predisposition to or
the presence of autism in a subject, the method comprising
genotyping a SNP in the gene loci of at least HTR5A, MACF1, RBFOX1,
ABR, PTPRG, CACNA2D1, GFRA1, DSCAML1, CHRM3, LPPR4, DLG2, SLC9A9
and BASP1 in a sample from said subject. In an embodiment, the
method comprises genotyping the single nucleotide polymorphism
(SNP) rs893109 in HTR5A (position 27 of SEQ ID NO: 31), and/or
genotyping the single nucleotide polymorphism (SNP) rs260969 in
MACF1 (position 27 on SEQ ID NO: 15), and/or genotyping the single
nucleotide polymorphism (SNP) rs12925135 in RBFOX1 (position 27 of
SEQ ID NO: 39), and/or genotyping the single nucleotide
polymorphism (SNP) rs2663327 in ABR (position 27 of SEQ ID NO: 40),
and/or genotyping the single nucleotide polymorphism (SNP) rs636624
in PTPRG (position 27 of SEQ ID NO: 22), and/or genotyping the
single nucleotide polymorphism (SNP) rs2367910 in CACNA2D1
(position 27 of SEQ ID NO: 41), and/or genotyping the single
nucleotide polymorphism (SNP) rs10787637 in GFRA1 (position 27 of
SEQ ID NO: 4), and/or genotyping the single nucleotide polymorphism
(SNP) rs695083 in DSCAML1 (position 27 of SEQ ID NO: 24), and/or
genotyping the single nucleotide polymorphism (SNP) rs10802802 in
CHRM3 (position 27 of SEQ ID NO: 5), and/or genotyping the single
nucleotide polymorphism (SNP) rs712886 in LPPR4 (position 27 of SEQ
ID NO: 27), and/or genotyping the single nucleotide polymorphism
(SNP) rs12275631 in DLG2 (position 27 of SEQ ID NO: 51), and/or
genotyping the single nucleotide polymorphism (SNP) rs3928471 in
SLC9A9 (position 27 of SEQ ID NO: 19), and/or genotyping the single
nucleotide polymorphism (SNP) rs298542 in BASP1 (position 27 of SEQ
ID NO: 52). Preferably, all genotyped SNPs are those mentioned in
previous sentence.
[0064] In a particular embodiment, the method further comprises
genotyping a SNP in the gene loci of any or all of KCNIP1, UGCG,
NTRK3, PLCB1, NELL1, GPR98, MAGI2, PLAGL1, CNTN6, DLG4, ERC2,
TRIM9, SYT14, JARID2, CDH13, SULF2, GRIN2A and NRG3, or
combinations thereof. In a preferred embodiment, the method further
comprises genotyping any or all of the SNP rs12514116 in KCNIP1
(position 27 on SEQ ID NO: 38), the SNP rs16916456 in UGCG
(position 27 of SEQ ID NO: 11), the SNP rs7172184 in NTRK3
(position 27 of SEQ ID NO: 28), the SNP rs8123323 in PLCB1
(position 27 of SEQ ID NO: 37), the SNP rs10766739 in NELL1
(position 27 of SEQ ID NO: 3), the SNP rs16868972 in GPR98 position
27 of SEQ ID NO: 42), the SNP rs12535987 in MAGI2 (position 27 of
SEQ ID NO: 43), the SNP rs207668 in PLAGL1 (position 27 of SEQ ID
NO: 12), the SNP rs9837484 in CNTN6 (position 27 of SEQ ID NO: 35),
the SNP rs314253 in DLG4 (position 27 of SEQ ID NO: 17), the SNP
rs1485677 in ERC2 (position 27 of SEQ ID NO: 8), the SNP rs10150121
in TRIM9 (position 27 of SEQ ID NO: 1), the SNP rs7534723 in SYT14
(position 27 of SEQ ID NO: 30), the SNP rs9370809 in JARID2
(position 27 of SEQ ID NO: 33), the SNP rs9940922 in CDH13
(position 27 of SEQ ID NO: 36), the SNP rs6063144 in SULF2
(position 27 of SEQ ID NO: 53), the SNP rs4782109 in GRIN2A
(position 27 of SEQ ID NO: 21), and the SNP rs2820100 in NRG3
(position 27 of SEQ ID NO: 54). Preferably, all genotyped SNPs are
those mentioned in previous sentence.
[0065] In another particular embodiment, the method further
comprises genotyping a SNP in the gene loci of any or all of NRG1,
TRIM2, EPHA5, PCDH10, HIP1, APBA1, PDE4D and EGLN3, or combinations
thereof. In a another preferred embodiment, the method further
comprises genotyping any or all of the SNP rs723811 in NRG1
(position 27 on SEQ ID NO: 44), the SNP rs11139294 in APBA1
(position 27 of SEQ ID NO: 6), the SNP rs11942354 in TRIM2
(position 27 of SEQ ID NO: 45), the SNP rs1597611 in EPHA5
(position 27 of SEQ ID NO: 10), the SNP rs4404561 in PCDH10
(position 27 of SEQ ID NO: 20), the SNP rs6962352 in HIP1 (position
27 of SEQ ID NO: 25), the SNP rs7075400 in NRG3 (position 27 of SEQ
ID NO: 55), the SNP rs35284 in PDE4D (position 27 of SEQ ID NO: 18)
and the SNP rs946630 in EGLN3 (position 27 of SEQ ID NO: 56).
Preferably, all genotyped SNPs are those mentioned in previous
sentence.
[0066] In a preferred embodiment, the method further comprises the
additional genotyping of at least one SNP in the gene loci selected
from the group consisting of ABR, ACCN1, AKAP7, APBA1, ASTN2,
BASP1, CACNA2D1, CADM1, CDH13, CHRM3, CNTN6, DCLK1, DCLK2, DLG2,
DLG4, DSCAML1, EGLN3, EPHA5, ERC2, GFRA1, GPR98, GRIN2A, GRIN2B,
GRM7, HIP1, HTR5A, JARID2, KCNH5, KCNIP1, LPPR4, MACF1, MAGI2,
MAP1S, MAP2K1, NAV2, NELL1, NRG1, NRG3, NTRK3, PAX2, PCDH10,
PDE11A, PDE4D, PLAGL1, PLCB1, PTPRD, PTPRG, RBFOX1, RGS6, SLC24A2,
SLC9A9, SULF2, SYT14, TRIM2, TRIM9 and UGCG, or combinations
thereof.
[0067] More preferably, the method further comprises genotyping a
SNP in the gene loci of any or all of KCNH5, MAP1S, GRM7, PAX2,
PTPRD, PDE11A, RGS6, ASTN2, ACCN1, DCLK2, SLC24A2, AKAP7, DCLK1,
MAP2K1, CADM1, and NAV2. In a still preferred embodiment, the
method further comprises the additional genotyping of at least one
SNP selected from the group consisting of KCNH5 is rs1041644
(position 27 of SEQ ID NO: 2), MAP1S is rs12985015 (position 27 of
SEQ ID NO: 7), GRM7 is rs1569284 (position 27 of SEQ ID NO: 9),
PAX2 is rs2077642 (position 27 of SEQ ID NO: 13), PTPRD is
rs2382104 (position 27 of SEQ ID NO: 14), PDE11A is rs2695112
(position 27 of SEQ ID NO: 16), RGS6 is rs6574041 (position 27 of
SEQ ID NO: 23), ASTN2 is rs7021928 (position 27 of SEQ ID NO: 26),
ACCN1 is rs7225320 (position 27 of SEQ ID NO: 29), DCLK2 is
rs9307866 (position 27 of SEQ ID NO: 32), SLC24A2 is rs957910
(position 27 of SEQ ID NO: 34), AKAP7 is rs6923644 (position 27 of
SEQ ID NO: 46), DCLK1 is rs1556060 (position 27 of SEQ ID NO: 47),
MAP2K1 is rs1432443 (position 27 of SEQ ID NO: 48), CADM1 is
rs220836 (position 27 of SEQ ID NO: 49), GRIN2B is rs7974275
(position 27 of SEQ ID NO: 50) and NAV2 is rs10500866 (position 27
of SEQ ID NO: 57). Preferably, all genotyped SNPs are those
mentioned in previous sentence.
[0068] In particular, the method may comprise genotyping of at
least one SNP in all of the following the gene loci: ABR, ACCN1,
AKAP7, APBA1, ASTN2, BASP1, CACNA2D1, CADM1, CDH13, CHRM3, CNTN6,
DCLK1, DCLK2, DLG2, DLG4, DSCAML1, EGLN3, EPHA5, ERC2, GFRA1,
GPR98, GRIN2A, GRIN2B, GRM7, HIP1, HTR5A, JARID2, KCNH5, KCNIP1,
LPPR4, MACF1, MAGI2, MAP1S, MAP2K1, NAV2, NELL1, NRG1, NRG3, NTRK3,
PAX2, PCDH10, PDE11A, PDE4D, PLAGL1, PLCB1, PTPRD, PTPRG, RBFOX1,
RGS6, SLC24A2, SLC9A9, SULF2, SYT14, TRIM2, TRIM9 and UGCG.
[0069] Preferably, the SNP in HTR5A is rs893109 (position 27 of SEQ
ID NO: 31), MACF1 is rs260969 (position 27 of SEQ ID NO: 15),
RBFOX1 is rs12925135 (position 27 of SEQ ID NO: 39), ABR is
rs2663327 (position 27 of SEQ ID NO: 40), PTPRG is rs636624
(position 27 of SEQ ID NO: 22), CACNA2D1 is rs2367910 (position 27
of SEQ ID NO: 41), GFRA1 is rs10787637 (position 27 of SEQ ID NO:
4), DSCAML1 is rs695083 (position 27 of SEQ ID NO: 24), CHRM3 is
rs10802802 (position 27 of SEQ ID NO: 5), LPPR4 is rs712886
(position 27 of SEQ ID NO: 27), DLG2 is rs12275631 (position 27 of
SEQ ID NO: 51), SLC9A9 is rs3928471 (position 27 of SEQ ID NO: 19),
BASP1 is rs298542 (position 27 of SEQ ID NO: 52), KCNIP1 is
rs12514116 (position 27 of SEQ ID NO: 38), UGCG is rs16916456
(position 27 of SEQ ID NO: 11), NTRK3 is rs7172184 (position 27 of
SEQ ID NO: 28), PLCB1 is rs8123323 (position 27 of SEQ ID NO: 37),
NELL1 is rs10766739 (position 27 of SEQ ID NO: 3), GPR98 is
rs16868972 (position 27 of SEQ ID NO: 42), MAGI2 is rs12535987
(position 27 of SEQ ID NO: 43), PLAGL1 is rs2076683 (position 27 of
SEQ ID NO: 12), CNTN6 is rs9837484 (position 27 of SEQ ID NO: 35),
DLG4 is rs314253 (position 27 of SEQ ID NO: 17), ERC2 is rs1485677
(position 27 of SEQ ID NO: 8), TRIM9 is rs10150121 (position 27 of
SEQ ID NO: 1), SYT14 is rs7534723 (position 27 of SEQ ID NO: 30),
JARID2 is rs9370809 (position 27 of SEQ ID NO: 33), CDH13 is
rs9940922 (position 27 of SEQ ID NO: 36), SULF2 is rs6063144
(position 27 of SEQ ID NO: 53), GRIN2A is rs4782109 (position 27 of
SEQ ID NO: 21), NRG3 is rs2820100 (position 27 of SEQ ID NO: 54) or
rs7075400 (position 27 of SEQ ID NO: 55), NRG1 rs723811 (position
27 of SEQ ID NO: 44), TRIM2 is rs11942354 (position 27 of SEQ ID
NO: 45), EPHA5 is rs1597611 (position 27 of SEQ ID NO: 10), PCDH10
is rs4404561 (position 27 of SEQ ID NO: 20), HIP1 is rs6962352
(position 27 of SEQ ID NO: 25), APBA1 is rs11139294 (position 27 of
SEQ ID NO: 6), PDE4D is rs35284 (position 27 of SEQ ID NO: 18),
EGLN3 is rs946630 (position 27 of SEQ ID NO: 56), KCNH5 is
rs1041644 (position 27 of SEQ ID NO: 2), MAP1S is rs12985015
(position 27 of SEQ ID NO: 7), GRM7 is rs1569284 (position 27 of
SEQ ID NO: 9), PAX2 is rs2077642 (position 27 of SEQ ID NO: 13),
PTPRD is rs2382104 (position 27 of SEQ ID NO: 14), PDE11A is
rs2695112 (position 27 of SEQ ID NO: 16), RGS6 is rs6574041
(position 27 of SEQ ID NO: 23), ASTN2 is rs7021928 (position 27 of
SEQ ID NO: 26), ACCN1 is rs7225320 (position 27 of SEQ ID NO: 29),
DCLK2 is rs9307866 (position 27 of SEQ ID NO: 32), SLC24A2 is
rs957910 (position 27 of SEQ ID NO: 34), AKAP7 is rs6923644
(position 27 of SEQ ID NO: 46), DCLK1 is rs1556060 (position 27 of
SEQ ID NO: 47), MAP2K1 is rs1432443 (position 27 of SEQ ID NO: 48),
CADM1 is rs220836 (position 27 of SEQ ID NO: 49), GRIN2B is
rs7974275 (position 27 of SEQ ID NO: 50) and/or NAV2 is rs10500866
(position 27 of SEQ ID NO: 57). Most preferably, all SNPs genotyped
are those mentioned in previous sentence.
[0070] The invention thus in particular provides a method of
determining a risk of autism, or of detecting the predisposition to
or presence of autism in a subject, the method comprising
genotyping of SNPs in a sample from said subject, wherein said SNPs
are rs2663327, rs7225320, rs6923644, rs11139294, rs7021928,
rs298542, rs2367910, rs220836, rs9940922, rs10802802, rs9837484,
rs1556060, rs9307866, rs12275631, rs314253, rs695083, rs946630,
rs1597611, rs1485677, rs10787637, rs16868972, rs4782109, rs7974275,
rs1569284, rs6962352, rs893109, rs9370809, rs1041644, rs12514116,
rs712886, rs260969, rs12535987, rs12985015, rs1432443, rs10500866,
rs10766739, rs723811, rs2820100, rs7075400, rs7172184, rs2077642,
rs4404561, rs2695112, rs35284, rs2076683, rs8123323, rs2382104,
rs636624, rs12925135, rs6574041, rs957910, rs3928471, rs6063144,
rs7534723, rs11942354, rs10150121, rs16916456.
[0071] Alternatively, the method may comprise genotyping at least
one SNP as set forth in any of SEQ ID NO:1 to SEQ ID NO:57.
[0072] The method may also further comprise genotyping a SNP in the
gene loci of any or all of PITX1, ATP2B2, EN2, JARID2, MARK1,
ITGB3, CNTNAP2, and HOXA1, or combinations thereof, preferably the
method further comprises genotyping any or all of the SNP selected
from the group consisting rs6872664, rs2278556, rs1861972,
rs7766973, rs12410279, rs5918, rs7794745, and rs10951154, or
combinations thereof. These genes and SNPs correspond to those
disclosed in Carayol et al, 2011 and in WO2011/138372. Indeed, the
addition of these genes/SNPs to the genotyping further slightly
improves the reliability of the test, as shown in the Examples.
[0073] In another embodiment, the presence of SNPs in linkage
disequilibrium (LD) with the above identified SNPs may be
genotyped, in place of, or in addition to, said identified SNPs. In
the context of the present invention, the SNPs in linkage
disequilibrium with the above identified SNP are within the same
gene of the above identified SNP.
[0074] The method of the invention, also referred to as "the test"
thus preferably includes genotyping all identified SNPs, or
subcombinations thereof. The test can be used to strengthen the
diagnosis by confirming a known risk profile. In such case a
negative test result does not invalidate the diagnosis for
autism.
[0075] Alternatively the test can be used to establish a detailed
risk profile for a non-diagnosed patient, who may be a sibling of
an individual diagnosed with autism, or not. A possible outcome is
defined as the presence of a risk allele in one or more SNPs, in a
heterozygous or homozygous status, implicating increased risk.
[0076] Following Table 1 describes the SNPs and their
risk-/protective-associated alleles identified as being useful in
the present invention, in combination or in subcombinations.
[0077] Following Table 2 describes the SNPs and their
risk-/protective-associated alleles identified as being useful in
Carayol et al, 2011 and in WO2011/138372.
TABLE-US-00001 TABLE 1 Autism-associated SNPs in combination
Relative Pro- Position position tec- of SNP Gender to the Risk tive
SEQ in SEQ speci- SNP Gene gene Allele Allele dbSNP context
sequence Strand ID NO: ID NO: ficity rs10150121 TRIM9 intron C T
CTGTGCTACTTAATGTAGACCAC + 1 27 All CTG[C/T]TTGATTTCCTGAATGTG
GTCTATGT rs1041644 KCNH5 intron C A CACCATTTTAAAGAGTTTAACTA - 2 27
All AAT[A/C]AAATTCATCAATGTTTC CACTATGT rs10766739 NELL1 intron A G
ACACAATTGGCAAAACCCCCTGT + 3 27 All CAC[A/G]GTCAGTAAACTTTGAG
GACCTGCTC rs10787637 GFRA1 intron G A TGGATAGTTGGACTCTGCAACCT + 4
27 All ACT[A/G]AAACAAACATGTTAAAA ATTAAACA rs10802802 CHRM3 intron G
A TCTGTCTCATCCCTGGTCAGGAT + 5 27 Female GTA[A/G]GTATAAGTTTGAAGGAT
CAAAAAAT rs11139294 APBA1 intron G A TGCCCTCATGGAACTTACCCTGA + 6 27
All GGA[A/G]TTTACAATAGAAATAAT TAAACATA rs12985015 MAP1S 5' G A
CAGCAGTCCTGAGGGCTCAGGG + 7 27 All TTCC[A/G]TTTTCCCCACAAA
TGCCATCATCTG rs1485677 ERC2 intron G A TTTTAATTAATCCTCTGCAAGGA + 8
27 All ACC[A/G]AGTTTTGTTTGCCAT CATCTCCCCA rs1569284 GRM7 intron G A
AAAGTCTATTTATTTTCCCAACTA - 9 27 All AT[A/G]TGTGTATGCTTCATGAG
AGCACAGC rs1597611 EPHA5 intron G A AGTGAGAAGTTATGGTGCTTTCT + 10 27
All CTC[A/G]CCTGCCTATGGCTGCC CACAGTCCA rs16916456 UGCG intron C T
TGATGTCAGTGATTGTAACTGTC + 11 27 Male ATT[C/T]CTAGAACTTGTGTGGTT
CTTTTCAT rs2076683 PLAGL1 intron T G AATGAACTGACACTGCACAACAA + 12
27 All GAC[G/T]GTCACATAAAACCACA GGAATCACT rs2077642 PAX2 intron T C
CTCCTGTAGGAGGCTTGAGCCT + 13 27 Female GGGT[C/T]TAGGTTGGAGACAGA
GGCCGAGAAG rs2382104 PTPRD intron G T ATGGACAGCCTATAGGCTGTAA + 14
27 All CCTG[G/T]TATTAGGAATAAAGCT TTCCCTATA rs260969 MACF1 intron T
C GTAAACCCTCCTCCTCTTTTAAG + 15 27 All TGC[C/T]GCCTCCTCTGTCCTTAA
TGCCCCCA rs2695112 PDE11A intron A G TCATCCCCCCATGTCGACCTAAA - 16
27 Female AGA[A/G]CACAGTTTACTTTTTCA AGGTTCTC rs314253 DLG4 3' G A
AAGTTGAGAGTTTCATGCAAAAG - 17 27 All ACC[A/G]ACCCAGGGGTAGTGAT
TCTGTGGAT rs35284 PDE4D intron A G CGTTGTAATTCTATCTTCAGAAT + 18 27
All GAT[A/G]CATTGCAAAAGAGTGT GACAAAAGG rs3928471 SLC9A9 intron T C
GCTTCATGTTTATGTTCTATGTT + 19 27 All CAG[C/T]TTTTGGTCTGTT rs4404561
PCDH10 5' C A TACCAGGGCCTGCTCAGCAACC + 20 27 All
AGAG[A/C]AGCAGAATGGGGGG CCAGATGCAAG rs4782109 GRIN2A intron T C
ATAGCAATGATGGACAAACTCCC + 21 27 All TGC[C/T]TCATGGGGCTTGCATT
TTAGAGCTA rs636624 PTPRG intron A G TTGGTCACTGCCTATTTCAATTC - 22 27
All TGG[A/G]TTTCTCTAACAATGAGA AATGGTCT rs6574041 RGS6 intron A G
GTAGTAGGATGTTTGAAGAACA + 23 27 All GCAA[A/G]GAGACCAGTGTGGCT
GGAAGAAGGG rs695083 DSCAML1 intron T C ACTGGTTTGAGGTCTCCCCCTG - 24
27 Female GAGC[C/T]ACCCAGAACACATCC AGGCCCTACC rs6962352 HIP1 intron
G A GGGTAGCAGTGGCTGTCCCTGT + 25 27 All GGGT[A/G]AAGTCACCTGACCAG
CCACTGTGAG rs7021928 ASTN2 intron A G GAACTGTAGCAGGTTCTTTGACA + 26
27 All TGT[A/G]TTATTTTATATCACAAC AAGCAGAG rs712886 LPPR4 intron C T
TAGTGAATAAGAGATAGACTTGG - 27 27 Female TCA[C/T]CAAGGAGCAATAGCTT
AGTGAGAGA rs7172184 NTRK3 intron C T AGATCTAGCTCTCTCAGGCACAA + 28
27 All ACA[C/T]CCAGATATTTTGTGATA GAAGGAAA rs7225320 ACCN1 intron T
C TCCCCTTTCTTAAGGATACAGAC + 29 27 All TTT[C/T]ATCAGCAGTGATCACTC
ATTCACCA rs7534723 SYT14 intron A G TGTACTCTTGCATGAAACCAGGA + 30 27
Female GAA[A/G]GTTTTACTTGGTTTGCT AAACTTTG rs893109 HTR5A 3' G A
AATATGCGAACTTTCACTTAAAA + 31 27 Male AGT[A/G]GGGAAAATATAGGATC
TCTGAATGC rs9307866 DCLK2 intron T C TAGTGCAAGAGGGGGATGTTTG + 32 27
All GTAT[C/T]GTGGATTGCACAGTG ACCTTGTTTT rs9370809 JARID2 intron T C
TTGGAGGGCATGCTGGTTGCAA + 33 27 All CCCT[C/T]TTATTCTAATAAGGAA
CTGGTTTGG rs957910 SLC24A2 intron T C CCTCCTGACAGTGTTGTGTCCT - 34
27 All GTAA[C/T]TGAAAGAGGATTGCA TCTGCACTCA rs9837484 CNTN6 intron G
A ACCGACATCAGTGGTCCATTCA + 35 27 Female GTGG[A/G]CCATTAATGTTGCCT
GACATTAAGT rs9940922 CDH13 intron G A TGTAGCCTCCAGGGTTGCTGTG + 36
27 Female GAAG[A/G]GAAAGAGAGAGCAAG GAGGGTCTTG rs8123323 PLCB1
intron T C TGATTTGAACCGTCAATACCAAC + 37 27 All
CCC[C/T]TAACCCCAGTAAAAAAA AAAACAGC rs12514116 KCNIP1 intron C T
TGGGGCCTCCTGGTGCTCCTCA + 38 27 Male GAAT[C/T]ACGTGCTCTGGGCAG
GAAAAAGGTG rs12925135 RBFOX1 intron C T TTTCTAATCCTTACCTCTCAGAG +
39 27 Male GGA[C/T]GATTATGAGAAGGAAA TGAACTATC rs2663327 ABR intron
C T CTTGTCTCTGCCCTGAAGCACA - 40 27 All GCCA[C/T]GTGGGTCTGAGAATC
CCCTACTTCC rs2367910 CACNA2D1 intron C A TTTTTAAATCTTTTTCTTTGTGAA +
41 27 Male CA[A/C]GTAAATTGAAATGAAAAG CTGAGTA rs16868972 GPR98
missense T G AACATCACACTATCAATAATAAG + 42 27 Male
GTT[G/T]AAAGGCCTCATGGGAA AAGTCCTTG rs12535987 MAGI2 intron C T
TAAGTTTTGTGGGCACTATTGAT + 43 27 Male AAA[C/T]AAAATGAAAGATAAAGA
CAAATGAA rs723811 NRG1 intron T C CTCACAATTCAATGTTTTAGCAT - 44 27
Male ATA[C/T]CATCAGGCAAAACTATC AATTTTGA rs11942354 TRIM2
near-gene-5 A G GTGGCGGTGATTCCCAGGTCTG + 45 27 Male
GTTG[A/G]TCAAGACTGCAATGC ACAACAGGAA rs6923644 AKAP7 intron G T
GGAAGTAATTGTATTGCATTAAT + 46 27 Male CAG[G/T]ACCATTATTTAGTATTG
GACATTTC rs1556060 DCLK1 near-gene-3 G A CTCACTCAACAGGTTTCAATGGG -
47 27 Male GGA[A/G]CAAATAACAATACGCA AGGTTAATA rs1432443 MAP2K1
intron T G CCTTGTAACACTACAGAAGGATA - 48 27 Male
TGT[G/T]AGGATTAGAGAATTTTA GCACTGGA rs220836 CADM1 intron G A
ATATATTTTACAGTAGTTGTCAAT - 49 27 Male CT[A/G]TTTTCCAGTTTTTCTGGT
ACTTTTT rs7974275 GRIN2B intron G T TAATGTAACAACCAACTGGTCTC + 50 27
Male CAT[G/T]TCCTTATAGGATTAAAA GCTATTAA rs12275631 DLG2 intron T C
ATATAGTGAGAAATATTACTGAT + 51 27 Female GAG[C/T]GGACTGAAACTGTTCA
TTGCATATT rs298542 BASP1 intron C T CAACGGGTAGGAAAGGACAGTT + 52 27
Female GGTT[C/T]AGTGCTTGCTCATGTT AGCCCTGTA rs6063144 SULF2 intron C
T ACTGCCGTAAATCACTGACTTTG + 53 27 Female AGC[C/T]TCAGCTTCCCTGTCTG
TAAAAACAC rs2820100 NRG3 intron C A ATGTCATTAGTCTTTGACCAATA + 54 27
Female TTT[A/C]TCCAGTCCTTATCCAGC CCCAGTTC rs7075400 NRG3 intron T C
GTTTGGGGAATATGTTTTTAGAA + 55 27 Female ATA[C/T]ACATGCCATATGTGAG
GCTATAGAA rs946630 EGLN3 unknown G A TCTTTGGGGAACTGAAAGAAGC + 56 27
Female CCTG[A/G]AGAACACAATATACA ATGGCACACC rs10500866 NAV2 intron T
C CAGAGCTGGGCATATACAGTAG + 57 27 Female GAGA[C/T]GTTTGCTATATTTTAG
GTAATTAAT << All >> means a SNP associated to autism in
males and females; << Males >> and << Females
>> mean a SNP associated to autism in males or in females
respectively.
TABLE-US-00002 TABLE 2 Autism-associated SNPs disclosed in Carayol
et al, 2011 and in WO2011/138372 Pro- Position Relative tec- of SNP
Gender position Risk tive SEQ in SEQ speci- SNP Gene to the gene
Allele Allele dbSNP context sequence Strand ID NO: ID NO: ficity
rs6872664 PITX1 intron C T TGCTTTTCTGAACTAGGATCA + 58 27 Male
GATCT[C/T]TCCAGCCTAAAG TCCCTCCACTTTC rs2278556 ATP2B2 intron A G
TTACGTGCCTATCATCCAGCT + 59 27 Male TTGTA[A/G]CATCTTAACATTA
TGCCGTACTTGC rs1861972 EN2 intron A G AGAGGCGAGGTCACCACTCC + 60 27
All CTGCCA[A/G]TGGCCTTGCCC CCTTCTTCCCCCAC rs7766973 JARID2 intron C
T CCCAGAGGGTTTATATTTTAC + 61 27 All CTGCA[C/T]TCCTGAGGATGT
GTTTGTGTTGCTT rs12410279 MARK1 3' A G AGTACTGCAAAACAGGACAG + 62 27
Female CCATCA[A/G]AGATTCTTCCCT GATGACATCTCAG rs5918 ITGB3 intron T
C GGCTCCTGTCTTACAGGCCC + 63 27 Female TGCCTC[C/T]GGGCTCACCTC
GCTGTGACCTGAAG rs7794745 CNTNAP2 intron T A ACAGGTCAGGACCTGGAAAG +
64 27 All GCCTAA[A/T]TGATAAGACTAA GTGTCAAAATCAG rs10951154 HOXA1
exon T C TCTGGTAGGTAGCCGGCTGG + 65 27 Male GGGTGG[C/T]GATGGTGGTG
GTGGTGGTGGTGGTG << All >> means a SNP associated to
autism in males and females; << Males >> and <<
Females >> mean a SNP associated to autism in males or in
females respectively.
Risk Determination
[0078] Once SNPs of interest have been genotyped, a risk of autism,
a predisposition to or the presence of autism in the tested subject
is determined.
[0079] In the methods of the invention, detecting the combined
presence of risk-associated alleles, preferably as defined in Table
1, is indicative of a risk of autism, a predisposition to autism,
or presence of autism in a subject. The risk level or the
likelihood of predisposition or presence of autism is determined
depending on the number of risk-associated alleles that are
detected, preferably by calculating a genetic score. The genetic
score (GS) is then compared to one or more threshold value(s).
[0080] A genetic score is first calculated based on the risk or
protective nature of each genotyped SNP. Table 1 defines the risk
and protective alleles of each of the specific 57 SNPs associated
to autisms in the present invention.
[0081] A genetic score is calculated by making an optionally
weighted sum of the risk-associated genotyped SNPs.
[0082] More precisely, when n distinct SNPs are genotyped, a
genetic score may be calculated using the following formula:
GS = i = 1 n x i ##EQU00001##
wherein each x.sub.i, 1.ltoreq.i.ltoreq.n, is the weight of each
genotyped SNPi.
[0083] Since any SNP will be genotyped for both alleles of the
subject, the participation of each SNP to the genetic score may be
weighted depending on the underlying genetic model of association
of the SNP to autism.
[0084] Three genetic models are possible: an additive model, a
recessive model and a dominant model. In a recessive model, only
the presence of two risk alleles will impact the autism risk. In a
dominant model, the presence of one or two risk alleles will
similarly impact the autism risk. Finally, in an additive model,
the presence of one risk allele will impact the autism risk, while
the presence of a further second risk allele will further impact
the autism risk.
[0085] Generally, an additive model is assumed as default model to
modelize the genotype of individuals for each SNPs analyzed in an
association study for statistical purpose (Pereira, Patsopoulos et
al. 2009). Under this model, each tested SNPi participates to the
genetic score as follows: x.sub.i=0 for "no risk allele", 1
x.sub.i=1 for "one risk allele" and x.sub.i=2 for "two risk
alleles". This case corresponds to the simpler genetic score, which
then corresponds to the sum of risk alleles genotyped in the
sample.
[0086] Such an additive default model may be used in the context of
the present invention, and still permits reasonable reliability of
the risk determination (see Examples). However, one way to weight
the SNPs in the genetic score and to improve the reliability of the
test consists in using their true underlying genetic model. When
SNPi is recessive, it adds 0 point to the genetic score (x.sub.i=0)
if the individual is homozygous non carrier of the risk allele and
heterozygous, and 2 points (x.sub.i=2) if he is homozygous carrier
of the risk allele. Similarly, when the SNP is dominant, it adds 0
point to the genetic score (x.sub.i=0) if the individual is
homozygous non carrier of the risk allele, and 2 points (x.sub.i=2)
otherwise.
[0087] The choice of the best genetic model for a given SNPi may be
made based on analysis of a reference (training) population of
samples, as described in the examples. For a proportion of SNPs,
all three genetic models or two alternative genetic models may be
used without significant impact on the reliability of the test.
[0088] The values of x.sub.i depending on the selected genetic
model and the number (0, 1 or 2) of risk-associated alleles
genotyped are summarized in following Table 3.
TABLE-US-00003 TABLE 3 SNPi weight (xi) as function of the genetic
model and number (0, 1 or 2) of risk-associated alleles genotyped
in the subject sample. We assume that allele 2 is the risk allele
for SNPi Genotype "1 1" Genotype "1 2" Genotype "2 2" (homozygous
or "2 1" (homozygous Genetic model protective allele)
(heterozygous) risk allele) Additive 0 1 2 Recessive 0 0 2 Dominant
0 2 2
[0089] Another weighting consists in using odds ratios estimated
for each genotype using the homozygous non carrier as reference: as
described in Table 4 below where OR.sub.11 equal 1 as the reference
genotype, OR.sub.het is the odds ratio associated to the
heterozygous genotype and OR.sub.hom is the odds ratio associated
to the homozygous carrier genotype. Odds ratio may be estimated
using classical logistic regression in the discovery (training)
population.
TABLE-US-00004 TABLE 4 SNPi weight (x.sub.i) as function of the
odds ratio. We assume that allele 2 is the risk allele for SNPi and
genotype "1 1" is the reference genotype Genotype "1 2" Genetic
model Genotype "1 1" or "2 1" Genotype "2 2" Odds ratio 1
OR.sub.het OR.sub.hom
[0090] While this weighting may, contrary to the mere selection of
an appropriate genetic model, take into account the fact that some
SNPs may impact the autism risk more than others, as explained in
the introduction, the contribution to disease risk of each
individual SNP is generally low, and the use of weights based on
odds ratio does not significantly improve the reliability of the
test.
[0091] Therefore, advantageously, when n distinct SNPs are
genotyped, a genetic score may be calculated using the following
formula:
GS = i = 1 n x i ##EQU00002##
[0092] wherein each x.sub.i, 1.ltoreq.i.ltoreq.n, is the weight of
each genotyped SNPi defined based on an additive, a recessive or a
dominant genetic model (see Table 3). In an embodiment, each
x.sub.i, 1.ltoreq.i.ltoreq.n, is the weight of each genotyped SNPi
defined based on an additive genetic model (see Table 3). In a
preferred embodiment, each x.sub.i, 1.ltoreq.u.ltoreq.n, is the
weight of each genotyped SNPi defined based on an additive, a
recessive or a dominant genetic model (see Table 3), wherein said
additive, recessive or dominant genetic model has been selected
based on the analysis of a reference (or discovery or training)
population of samples (see Examples).
[0093] The obtained genetic score is then compared to one or more
threshold (or cut-off) values in order to define an autism risk
level.
[0094] Depending on the number of threshold values, two or more
categories of subjects will be defined. Preferably, the number of
threshold values is comprised between 1 and 4. In particular, 1, 2,
3, or 4 threshold values may be used.
[0095] For one threshold value, two categories of subjects will be
defined: [0096] Below the threshold value, a category of subjects
with a lower risk of autism than the prevalence of autism in the
reference population of subjects, [0097] Above the threshold value,
a category of subjects with a higher risk of autism than the
prevalence of autism in the reference population of subjects.
[0098] By "reference population of subjects" it is meant either the
general population (including any individual) or the population of
subjects having a sibling with an autism spectrum disorder. The
reference population will be selected depending on the nature of
the tested subject. If the tested subject is not related to anyone
with an autism-spectrum disorder, then the reference population
will be the general population (including any individual), in which
the prevalence of autism is about 1 per 110 children (i.e. 9.1%).
Alternatively, if the tested subject is a sibling of an individual
with an autism spectrum disorder, then the reference population
will be the population of subjects having a sibling with an autism
spectrum disorder, in which the prevalence of autism is about
19%.
[0099] The selection of an appropriate threshold value is made
based on analysis of a reference (or discovery or training)
population of samples, and depending on which feature(s) of the
test (specificity, sensitivity, positive predictive value, negative
predictive value) is/are considered as the most important. Indeed,
features of a test based on a quantitative genetic score can be
altered by changing the threshold or cut-off value. Lowering the
threshold improves the sensitivity of the test but at the price of
lower specificity and more false-positive results. Inversely,
raising the cut-off improves the specificity at the price of lower
sensitivity and more false negative results.
[0100] A multi-risk class test may be constructed using more than
one threshold value: [0101] Two threshold values (V1 and V2) may be
set to create 3 classes of risk: a reference class
(V1.ltoreq.GS<V2) where the risk is close or equal to the
prevalence of the disease in the reference population of subjects,
a low risk class (GS<V1) where the risk is lower than the risk
in the reference class, and a high risk class (GS.gtoreq.V2) where
the risk is higher than in the reference class. [0102] Three
threshold values (V1, V2 and V3) may be set to create 4 classes of
risk: a high risk class (V2.ltoreq.GS<V3), where the risk is
higher than the prevalence of the disease in the reference
population of subjects; a very high risk class (GS.gtoreq.V3) where
the risk is much higher than the prevalence of the disease in the
reference population of subjects; a low risk class
(V1.ltoreq.GS<V2) were the risk is lower than the prevalence of
the disease in the reference population of subjects; and a very low
risk class (GS<V1) were the risk is much lower than the
prevalence of the disease in the reference population of subjects.
[0103] Four threshold values (V1, V2, V3 and V4) may be set to
create 5 classes of risk: a reference class (V2.ltoreq.GS<V3)
where the risk is close to the prevalence of the disease in the
reference population of subjects; a high risk class
(V3.ltoreq.GS<V4), where the risk is higher than in the
reference class; a very high risk class (GS.gtoreq.V4) where the
risk is much higher than in the reference class; a low risk class
(V1.ltoreq.GS<V2) were the risk is lower than the risk in the
reference class; and a very low risk class (GS<V1) were the risk
is much lower than the risk in the reference class.
[0104] The number and the value of the different threshold values
are settled according to the performance and characteristics
expected for the test defined by risk in classes, sensitivity and
specificity. Practical examples of determination of one or several
appropriate threshold value(s) are described in the experimental
section.
[0105] Alternatively, a diagnosis of risk of autism, or of a
predisposition to autism or of the presence of autism may generally
be made if all genotyped SNPs include at least one risk-associated
allele. If an additive default genetic model is selected, this
corresponds to a genetic score of at least half the maximum genetic
score.
[0106] For instance, when the genotyped SNPs are rs893109,
rs260969, rs12925135, rs2663327, rs636624, rs2367910, rs10787637,
rs695083, rs10802802, rs712886, rs12275631, rs3928471 and rs298542,
then the subject has a risk of or is predisposed to or has autism
when at least one allele of rs893109 is G, at least one allele of
rs260969 is T, at least one allele of rs12925135 is C, at least one
allele of rs2663327 is C, at least one allele of rs636624 is A, at
least one allele of rs2367910 is C, at least one allele of
rs10787637 is G, at least one allele of rs695083 is T, at least one
allele of rs10802802 is G, at least one allele of rs712886 is C, at
least one allele of rs12275631 is T, at least one allele of
rs3928471 is T and at least one allele of rs298542 is C.
[0107] Similarly, when the genotyped SNPs further include
rs12514116, rs16916456, rs7172184, rs8123323, rs10766739,
rs16868972, rs12535987, rs2076683, rs9837484, rs314253, rs1485677,
rs10150121, rs7534723, rs9370809, rs9940922, rs6063144, rs4782109
and rs2820100, then the subject has or is predisposed to autism
when, in addition to the above, at least one allele of rs12514116
is C, at least one allele of rs16916456 is C, at least one allele
of rs7172184 is C, at least one allele of rs8123323 is T, at least
one allele of rs10766739 is A, at least one allele of rs16868972 is
T, at least one allele of rs12535987 is C, at least one allele of
rs2076683 is T, at least one allele of rs9837484 is G, at least one
allele of rs314253 is G, at least one allele of rs1485677 is G, at
least one allele of rs10150121 is C, at least one allele of
rs7534723 is A, at least one allele of rs9370809 is T, at least one
allele of rs9940922 is G, at least one allele of rs6063144 is C, at
least one allele of rs4782109 is T and at least one allele of
rs2820100 is C.
[0108] Similarly, when the genotyped SNPs further include rs723811,
rs11139294, rs11942354, rs1597611, rs4404561, rs6962352, rs7075400,
rs35284 and rs946630, then the subject has or is predisposed to
autism when, in addition to the above, at least one allele of
rs723811 is T, at least one allele of rs11139294 is G, at least one
allele of rs11942354 is A, at least one allele of rs1597611 is G,
at least one allele of rs4404561 is C, at least one allele of
rs6962352 is G, at least one allele of rs7075400 is T, at least one
allele of rs35284 is A and at least one allele of rs946630 is
G.
[0109] Similarly, when the genotyped SNPs further include
rs1041644, rs12985015, rs1569284, rs2077642, rs2382104, rs2695112,
rs6574041, rs7021928, rs7225320, rs9307866, rs957910, rs6923644,
rs1556060, rs1432443, rs220836, rs7974275 and rs10500866, then the
subject has or is predisposed to autism when, in addition to the
above, at least one allele of rs1041644 is C, at least one allele
of rs12985015 is G, at least one allele of rs1569284 is G, at least
one allele of rs2077642 is T, at least one allele of rs2382104 is
G, at least one allele of rs2695112 is A, at least one allele of
rs6574041 is A, at least one allele of rs7021928 is A, at least one
allele of rs7225320 is T, at least one allele of rs9307866 is T, at
least one allele of rs957910 is T, at least one allele of rs6923644
is G, at least one allele of rs1556060 is G, at least one allele of
rs1432443 is T, at least one allele of rs220836 is G, at least one
allele of rs7974275 is G and at least one allele of rs10500866 is
T.
[0110] However, a risk of autism, a predisposition to or the
presence of autism in the tested subject is preferably determined
based on calculation of a genetic score (GS) and comparison of the
GS to one or more threshold values, as described above.
Linkage Disequilibrium (LD)
[0111] Once a first SNP has been identified in a genomic region of
interest, the practitioner of ordinary skill in the art can easily
identify additional SNPs in linkage disequilibrium with this first
SNP. In the context of the invention, the additional SNPs in
linkage disequilibrium with a first SNP are within the same gene of
said first SNP.
[0112] Linkage disequilibrium (LD) is defined as the non-random
association of alleles at different loci across the genome. Alleles
at two or more loci are in LD if their combination occurs more or
less frequently than expected by chance in the population.
[0113] For example, if a particular genetic element (e.g., an
allele of a polymorphic marker, or a haplotype) occurs in a
population at a frequency of 0.50 (50%) and another element occurs
at a frequency of 0.50 (50%), then the predicted occurrence of a
person's having both elements is 0.25 (25%), assuming a random
distribution of the elements. However, if it is discovered that the
two elements occur together at a frequency higher than 0.25, then
the elements are said to be in linkage disequilibrium, since they
tend to be inherited together at a higher rate than what their
independent frequencies of occurrence (e.g., allele or haplotype
frequencies) would predict.
[0114] When there is a causal locus in a DNA region, due to LD, one
or more SNPs nearby are likely associated with the trait too.
Therefore, any SNPs in LD with a first SNP associated with autism
or an associated disorder will be associated with this trait.
Identification of additional SNPs in linkage disequilibrium with a
given SNP involves: (a) amplifying a fragment from the gene
comprising a first SNP from a plurality of individuals; (b)
identifying of second SNPs in the gene comprising said first SNP;
(c) conducting a linkage disequilibrium analysis between said first
SNP and second SNPs; and (d) selecting said second SNPs as being in
linkage disequilibrium with said first marker. Subcombinations
comprising steps (b) and (c) are also contemplated.
[0115] Methods to identify SNPs and to conduct linkage
disequilibrium analysis can be carried out by the skilled person
without undue experimentation by using well-known methods.
[0116] Thus, the practitioner of ordinary skill in the art can
easily identify SNPs or combination of SNPs within haplotypes in
linkage disequilibrium with the at risk SNP.
[0117] Such markers are mapped and listed in public databases like
HapMap as well known to the skilled person. Genomic LD maps have
been generated across the genome, and such LD maps have been
proposed to serve as framework for mapping disease-genes (Risch et
al, 1996; Maniatis et al, 2002; Reich et al, 2001). If all
polymorphisms in the genome were independent at the population
level (i.e., no LD), then every single one of them would need to be
investigated in association studies, to assess all the different
polymorphic states. However, due to linkage disequilibrium between
polymorphisms, tightly linked polymorphisms are strongly
correlated, which reduces the number of polymorphisms that need to
be investigated in an association study to observe a significant
association. Another consequence of LD is that many polymorphisms
may give an association signal due to the fact that these
polymorphisms are strongly correlated.
[0118] The two metrics most commonly used to measure LD are D' and
r.sup.2 and can be written in terms of each other and allele
frequencies. Both measures range from 0 (the two alleles are
independent or in equilibrium) to 1 (the two allele are completely
dependent or in complete disequilibrium), but with different
interpretation. |D'| is equal to 1 if at most two or three of the
possible haplotypes defined by two markers are present, and <1
if all four possible haplotypes are present. r.sup.2 measures the
statistical correlation between two markers and is equal to 1 if
only two haplotypes are present.
[0119] Most SNPs in humans probably arose by single base modifying
events that took place within chromosomes many times ago. A single
newly created allele, at its time of origin, would have been
surrounded by a series of alleles at other polymorphic loci like
SNPs establishing a unique grouping of alleles (i.e. haplotype). If
this specific haplotype is transmitted intact to next generations,
complete LD exists between the new allele and each of the nearby
polymorphisms meaning that these alleles would be 100% predictive
of the new allele. Thus, because of complete LD (D'=1 or r.sup.2=1)
an allele of one polymorphic marker can be used as a surrogate for
a specific allele of another. Event like recombination may decrease
LD between markers. But, moderate (i.e. 0.5.ltoreq.r.sup.2<0.8)
to high (i.e. 0.8.ltoreq.r.sup.2<1) LD conserve the "surrogate"
properties of markers. In LD based association studies, when LD
exist between markers and an unknown pathogenic allele, then all
markers show a similar association with the disease. In a study by
Philippi et al (2007), a set of SNPs in strong LD has been shown to
be significantly associated to autism (Table 3 for association
results and FIG. 2 for LD plots in Philippi et al. (2007))
demonstrating that a set of 5 SNPs (rs1131611, rs11959298,
rs6872664, rs6596188 and rs6596189) could be used as surrogate
markers for an unknown pathogenic allele in LD with the 5 SNPs.
Similar results were observed for different association studies in
autism: for two SNPs in high LD within EN2 gene (r2>0.8 for
rs1861972 and rs1861973 in Gharani et al (2004)), ASMT gene
(D'=0.94 for rs4446909 and 5989681 in Melke et al. (2008)) or NRCAM
(four SNPs with D' between 0.64 and 1 in Marui et al. (2008)).
Alternatively, if one SNP did not provide association to the
disease, SNPs in high or moderate LD will not provide association:
among four SNPs flanking SP1 genes in high LD (r2 between 0.77 and
0.91) and 4 SNPs flanking SUB1 gene (r2 between 0.79 and 0.95),
none displayed any association to autism (Campbell et al. (2008))
suggesting an absence of pathogenic variant in LD with the
SNPs.
[0120] It is well known that many SNPs have alleles that show
strong LD (or high LD, defined as r.sup.2.gtoreq.0.80) with other
nearby SNP alleles and in regions of the genome with strong LD, a
selection of evenly spaced SNPs, or those chosen on the basis of
their LD with other SNPs (proxy SNPs or Tag SNPs), can capture most
of the genetic information of SNPs, which are not genotyped with
only slight loss of statistical power. In association studies, this
region of LD are adequately covered using few SNPs (Tag SNPs) and a
statistical association between a SNP and the phenotype under study
means that the SNP is a causal variant or is in LD with a causal
variant. It is a general consensus that a proxy (or Tag SNP) is
defined as a SNP in LD (r.sup.2.gtoreq.0.8) with one or more other
SNPs. The genotype of the proxy SNP could predict the genotype of
the other SNP via LD and inversely. In particular, any SNP in LD
with one of the SNPs used herein may be replaced by one or more
proxy SNPs defined according to their LD as r.sup.2.gtoreq.0.8.
[0121] These SNPs in linkage disequilibrium can also be used in the
methods according to the present invention, and more particularly
in the diagnostic methods according to the present invention. In
particular, the presence of SNPs in linkage disequilibrium (LD)
with the above identified SNPs may be genotyped, in place of, or in
addition to, said identified SNPs. In the context of the present
invention, the SNPs in linkage disequilibrium with the above
identified SNP are within the same gene of the above identified
SNP. Therefore, in the present invention, the presence of SNPs in
linkage disequilibrium (LD) with a SNP of interest and located
within the same gene as the SNP of interest may be genotyped, in
place of, or in addition to, said SNP of interest. Preferably, such
an SNP and the SNP of interest have r.sup.2.gtoreq.0.70, preferably
r.sup.2.gtoreq.0.75, more preferably r.sup.2.gtoreq.0.80, and/or
have D'.gtoreq.0.60, preferably D'.gtoreq.0.65, D'.gtoreq.0.7,
D'.gtoreq.0.75, more preferably D'.gtoreq.0.80. Most preferably,
such an SNP and the SNP of interest have r.sup.2.gtoreq.0.80, which
is used as reference value to define "LD" between SNPs.
Gender Specificity
[0122] The invention further provides a method of determining a
risk of autism, or of detecting the predisposition or presence of
autism in a male subject, the method comprising genotyping a SNP in
the gene loci of at least HTR5A, MACF1, RBFOX1, ABR, PTPRG, and
CACNA2D1, in a sample from said subject. Preferably, the SNP in
HTR5A is rs893109 (position 27 of SEQ ID NO: 31), in MACF1 is
rs260969 (position 27 of SEQ ID NO: 15), in RBFOX1 is rs12925135
(position 27 of SEQ ID NO: 39), in ABR is rs2663327 (position 27 of
SEQ ID NO: 40), in PTPRG is rs636624 (position 27 of SEQ ID NO:
22), and/or in CACNA2D1 is rs2367910 (position 27 of SEQ ID NO:
41). Most preferably, all SNPs genotyped are those mentioned in
previous sentence. Therefore, in male subjects, the method more
particularly comprises genotyping at least rs893109, rs260969,
rs12925135, rs2663327, rs636624 and rs2367910.
[0123] Preferably, the method further comprises genotyping a SNP in
the gene loci of any or all of KCNIP1, UGCG, NTRK3, PLCB1, NELL1,
GPR98, MAGI2, and PLAGL1, or combinations thereof. In this case,
advantageously, the SNP in KCNIP1 is rs12514116 (position 27 of SEQ
ID NO: 38), in UGCG is rs16916456 (position 27 of SEQ ID NO: 11),
in NTRK3 is rs7172184 (position 27 of SEQ ID NO: 28), in PLCB1 is
rs8123323 (position 27 of SEQ ID NO: 37), in NELL1 is rs10766739
(position 27 of SEQ ID NO: 3), in GPR98 is rs16868972 (position 27
of SEQ ID NO: 42), in MAGI2 is rs12535987 (position 27 of SEQ ID
NO: 43), and/or in PLAGL1 is rs2076683 (position 27 of SEQ ID NO:
12). Therefore, preferably, in male subjects, the method further
comprises genotyping any or all of the following SNPs rs12514116,
rs16916456, rs7172184, rs8123323, rs10766739, rs16868972,
rs12535987 and rs2076683, or combinations thereof.
[0124] More preferably, the method further comprises genotyping a
SNP in the gene loci of any or all of NRG1, TRIM2, EPHA5, PCDH10,
and HIP1, or combinations thereof. In this case, advantageously,
the SNP in NRG1 is rs723811 (position 27 of SEQ ID NO: 44), in
TRIM2 is rs11942354 (position 27 of SEQ ID NO: 45), in EPHA5 is
rs1597611 (position 27 of SEQ ID NO: 10), in PCDH10 is rs4404561
(position 27 of SEQ ID NO: 20), and/or in HIP1 is rs6962352
(position 27 of SEQ ID NO: 25). Most preferably, all SNPs genotyped
are those mentioned in previous sentence. Therefore, more
preferably, in male subjects, the method further comprises
genotyping any or all of the following SNPs rs723811, rs11942354,
rs1597611, rs4404561 and rs6962352, or combinations thereof.
[0125] Even more preferably, the method further comprises
genotyping a SNP in the gene loci of any or all of PDE11A, AKAP7,
DCLK1, KCNH5, GRIN2A, ACCN1, DCLK2, ASTN2, GRM7, MAP2K1, CADM1, and
GRIN2B, or combinations thereof. In this case, advantageously, the
SNP in PDE11A is rs2695112 (position 27 of SEQ ID NO: 16), in AKAP7
is rs6923644 (position 27 of SEQ ID NO: 46), near 3' of DCLK1 is
rs1556060 (position 27 of SEQ ID NO: 47), in KCNH5 is rs1041644
(position 27 of SEQ ID NO: 2), in GRIN2A is rs4782109 (position 27
of SEQ ID NO: 21), in ACCN1 is rs7225320 (position 27 of SEQ ID NO:
29), in DCLK2 is rs9307866 (position 27 of SEQ ID NO: 32), in ASTN2
is rs7021928 (position 27 of SEQ ID NO: 26), in GRM7 is rs1569284
(position 27 of SEQ ID NO: 9), in MAP2K1 is rs1432443 (position 27
of SEQ ID NO: 48), in CADM1 is rs220836 (position 27 of SEQ ID NO:
49), and/or in GRIN2B is rs7974275 (position 27 of SEQ ID NO: 50).
Most preferably, all SNPs genotyped are those mentioned in previous
sentence. Thus, the method preferably further comprises genotyping
any or all of the SNP selected from the group of rs2695112,
rs6923644, rs1556060, rs1041644, rs4782109, rs7225320, rs9307866,
rs7021928, rs1569284, rs1432443, rs220836, and rs7974275, or
combinations thereof. In a preferred embodiment, the invention
further provides a method of determining a risk of autism, or of
detecting the predisposition or presence of autism in a male
subject, the method comprising genotyping any SNP as identified in
Table 1 or in Table 5.
[0126] Table 5 (see below) describes the SNPs useful for the
detection of autism in males according to their degree of
reproducibility. Their AUCs (Area Under Curves) and associated
p-value are also provided.
[0127] In these methods, detecting the combined presence of
risk-associated alleles, preferably as defined in Table 1, is
indicative of a risk of autism, a predisposition to autism, or
presence of autism in the male subject. More particularly, the
autism risk level is determined as described above, by combining
the risk-associated SNPs into a genetic score and comparing it to
one or more threshold values. In particular, the combined presence
of a G for rs893109, a T for r rs260969, a C for rs12925135, a C
for rs2663327, a A for rs636624 and a C for rs2367910 is indicative
of a subject being at risk with, predisposed to, or having autism
(A genetic score built from these 6 SNPs as described in the
Example section is associated to a RI.gtoreq.0.95, an AUC of 0.64
with p=5.5.times.10.sup.-8).
[0128] The method may also further comprise genotyping a SNP in the
gene loci of any or all of PITX1, ATP2B2, EN2, JARID2, CNTNAP2, and
HOXA1, or combinations thereof, preferably the method further
comprises genotyping any or all of the SNP selected from the group
consisting rs6872664, rs2278556, rs1861972, rs7766973, rs7794745,
and rs10951154, or combinations thereof.
[0129] The invention further provides a method of determining a
risk of autism, or of detecting the predisposition or presence of
autism in a female subject, the method comprising genotyping a SNP
in the gene loci of at least CHRM3, DSCAML1, PTPRG, GFRA1, LPPR4,
DLG2, SLC9A9 and BASP1, in a sample from said subject. Preferably,
the SNP in CHRM3 is rs10802802 (position 27 of SEQ ID NO: 5), in
DSCAML1 is rs695083 (position 27 of SEQ ID NO: 24), in PTPRG is
rs636624 (position 27 of SEQ ID NO: 22), in LPPR4 is rs712886
(position 27 of SEQ ID NO: 27), in DLG2 is rs12275631 (position 27
of SEQ ID NO: 51), in SLC9A9 is rs3928471 (position 27 of SEQ ID
NO: 19), in BASP1 is rs298542 (position 27 of SEQ ID NO: 52). Most
preferably, all SNPs genotyped are those mentioned in previous
sentence. Therefore, in female subjects, the method more
particularly comprises genotyping at least rs10787637, rs636624,
rs695083, rs10802802, rs712886, rs12275631, rs3928471 and
rs298542.
[0130] Preferably, the method further comprises genotyping a SNP in
the gene loci of any or all of CNTN6, NTRK3, DLG4, ERC2, TRIM9,
SYT14, JARID2, CDH13, SULF2, GRIN2A and NRG3, or combinations
thereof. In this case, advantageously, the SNP in CNTN6 is
rs9837484 (position 27 of SEQ ID NO: 35), in NTRK3 is rs7172184
(position 27 of SEQ ID NO: 28), in DLG4 is rs314253 (position 27 of
SEQ ID NO: 17), in ERC2 is rs1485677 (position 27 of SEQ ID NO: 8),
in TRIM9 is rs10150121 (position 27 of SEQ ID NO: 1), in SYT14 is
rs7534723 (position 27 of SEQ ID NO: 30), in JARID2 is rs9370809
(position 27 of SEQ ID NO: 33), in CDH13 is rs9940922 (position 27
of SEQ ID NO: 36), in SULF2 is rs6063144 (position 27 of SEQ ID NO:
53), in GRIN2A is rs4782109 (position 27 of SEQ ID NO: 21), and/or
in NRG3 is rs2820100 (position 27 of SEQ ID NO: 54) or rs7075400
(position 27 of SEQ ID NO: 55). Most preferably, all SNPs genotyped
are those mentioned in previous sentence. Thus, preferably, in
female subjects, the method further comprises genotyping any or all
of the following SNPs rs9837484, rs7172184, rs314253, rs1485677,
rs10150121, rs7534723, rs9370809, rs9940922, rs6063144, rs4782109
and rs2820100, or combinations thereof.
[0131] More preferably, the method further comprises genotyping a
SNP in the gene loci of any or all of APBA1, ABR, NRG3, PDE4D and
EGLN3, or combinations thereof. In this case, advantageously, the
SNP in APBA1 is rs11139294 (position 27 of SEQ ID NO: 6), in ABR is
rs2663327 (position 27 of SEQ ID NO: 40), in NRG3 is rs7075400
(position 27 of SEQ ID NO: 55), in PDE4D is rs35284 (position 27 of
SEQ ID NO: 18), and/or in EGLN3 is rs946630 (position 27 of SEQ ID
NO: 56). Most preferably, all SNPs genotyped are those mentioned in
previous sentence. Thus, more preferably, in female subjects, the
method further comprises genotyping any or all of the following
SNPs rs11139294, rs2663327, rs7075400, rs35284 and rs946630, or
combinations thereof.
[0132] Even more preferably, the method further comprises
genotyping a SNP in the gene loci of any or all of RGS6, SLC24A2,
PTPRD, NAV2, PCDH10, MAP1S, and PAX2, or combinations thereof. In
this case, advantageously, the SNP in RGS6 is rs6574041 (position
27 of SEQ ID NO: 23), in SLC24A2 is rs957910 (position 27 of SEQ ID
NO: 34), in PTPRD is rs2382104 (position 27 of SEQ ID NO: 14), in
NAV2 is rs10500866 (position 27 of SEQ ID NO: 57), in PCDH10 is
rs4404561 (position 27 of SEQ ID NO: 20), in MAP1S is rs12985015
(position 27 of SEQ ID NO: 7), and/or in PAX2 is rs2077642
(position 27 of SEQ ID NO: 13). Most preferably, all SNPs genotyped
are those mentioned in previous sentence. Thus, the method
preferably further comprises genotyping any or all of the SNP
selected from the group consisting rs6574041, rs957910, rs2382104,
rs10500866, rs4404561, rs12985015, and rs2077642, or combinations
thereof. In a preferred embodiment, the invention further provides
a method of determining a risk of autism, or of detecting the
predisposition or presence of autism in a female subject, the
method comprising genotyping any SNP as identified in Table 1 or in
Table 6.
[0133] Table 6 (see below) describes the SNPs useful for the
detection of autism in females according to their degree of
reproducibility. Their AUCs and associated p-value are also
provided.
[0134] In these methods, detecting the combined presence of
risk-associated alleles, preferably as defined in Table 1, is
indicative of a risk of autism, a predisposition to autism, or
presence of autism in the female subject. More particularly, the
autism risk level is determined as described above, by combining
the risk-associated SNPs into a genetic score and comparing it to
one or more threshold values. In particular, the combined presence
of a G for rs10802802, a T for rs695083, a A for rs636624, a G for
rs10787637, a C for rs712886, a T for rs12275631, a T for rs3928471
and a C for rs298542 is indicative of a subject being at risk with,
predisposed to, or having autism (A genetic score built from these
8 SNPs as described in the Example section is associated to a
RI.gtoreq.0.95 an AUC of 0.69 with p=1.77.times.10.sup.-9).
[0135] The method may also further comprise genotyping a SNP in the
gene loci of any or all of EN2, JARID2, MARK1, ITGB3, and CNTNAP2,
or combinations thereof, preferably the method further comprises
genotyping any or all of the SNP selected from the group consisting
rs1861972, rs7766973, rs12410279, rs5918, and rs7794745, or
combinations thereof.
TABLE-US-00005 TABLE 5 AUCs and associated Pvalue for genetic score
built from SNPs with different degree of reproducibility in the
Discovery and Validation sample in males. RI RI .gtoreq. 0.95 RI
.gtoreq. 0.90 RI .gtoreq. 0.85 RI .gtoreq. 0.80 definition (6 SNPs)
(14 SNPs) (19 SNPs) (31 SNPs) AUC 0.64 0.68 0.70 0.7 Pvalue 5.5
.times. 10.sup.-8 3.04 .times. 10.sup.-8 6.01 .times. 10.sup.-13
2.6 .times. 10.sup.-12 SNP list rs893109 rs893109 rs893109 rs893109
rs260969 rs12514116 rs12514116 rs12514116 rs12925135 rs16916456
rs16916456 rs16916456 rs2663327 rs260969 rs260969 rs260969 rs636624
rs12925135 rs12925135 rs12925135 rs2367910 rs2663327 rs2663327
rs2663327 rs636624 rs636624 rs636624 rs7172184 rs7172184 rs7172184
rs2367910 rs2367910 rs2367910 rs8123323 rs8123323 rs8123323
rs10766739 rs10766739 rs10766739 rs16868972 rs16868972 rs16868972
rs12535987 rs12535987 rs12535987 rs2076683 rs2076683 rs2076683
rs723811 rs723811 rs11942354 rs11942354 rs1597611 rs1597611
rs4404561 rs4404561 rs6962352 rs6962352 rs2695112 rs6923644
rs1556060 rs1041644 rs4782109 rs7225320 rs9307866 rs7021928
rs1569284 rs1432443 rs220836 rs7974275
TABLE-US-00006 TABLE 6 AUCs and associated Pvalue for genetic score
built from SNPs with different degree of reproducibility in the
Discovery and Validation sample in females RI RI .gtoreq. 0.95 RI
.gtoreq. 0.90 RI .gtoreq. 0.85 RI .gtoreq. 0.80 definition (8 SNPs)
(19 SNPs) (24 SNPs) (31 SNPs) AUC 0.69 0.74 0.74 0.73 Pvalue 1.77
.times. 10.sup.-9 8.49 .times. 10.sup.-12 10.sup.-13 2.7 .times.
10.sup.-12 SNP list rs10787637 rs10787637 rs10787637 rs10787637
rs636624 rs636624 rs636624 rs636624 rs695083 rs695083 rs695083
rs695083 rs10802802 rs10802802 rs10802802 rs10802802 rs712886
rs712886 rs712886 rs712886 rs12275631 rs12275631 rs12275631
rs12275631 rs3928471 rs3928471 rs3928471 rs3928471 rs298542
rs298542 rs298542 rs298542 rs9837484 rs9837484 rs9837484 rs7172184
rs7172184 rs7172184 rs314253 rs314253 rs314253 rs1485677 rs1485677
rs1485677 rs10150121 rs10150121 rs10150121 rs7534723 rs7534723
rs7534723 rs9370809 rs9370809 rs9370809 rs9940922 rs9940922
rs9940922 rs6063144 rs6063144 rs6063144 rs4782109 rs4782109
rs4782109 rs2820100 rs2820100 rs2820100 rs11139294 rs11139294
rs2663327 rs2663327 rs7075400 rs7075400 rs35284 rs35284 rs946630
rs946630 rs6574041 rs957910 rs2382104 rs10500866 rs4404561
rs12985015 rs2077642
Genotyping Methods and Kits
[0136] The term "genotyping" means determining the allele of the
recited SNPs, which allows detecting the presence of a autism
risk-associated allele.
[0137] The SNP in the gene locus may be genotyped by sequencing,
selective hybridisation and/or selective amplification.
[0138] Sequencing can be carried out using techniques well known in
the art, using automatic sequencers. The sequencing may be
performed on the complete genes or, more preferably, on specific
domains thereof, typically those known or suspected to carry
deleterious mutations or other alterations.
[0139] Amplification is based on the formation of specific hybrids
between complementary nucleic acid sequences that serve to initiate
nucleic acid reproduction.
[0140] Amplification may be performed according to various
techniques known in the art, such as by polymerase chain reaction
(PCR), ligase chain reaction (LCR), strand displacement
amplification (SDA) and nucleic acid sequence based amplification
(NASBA). These techniques can be performed using commercially
available reagents and protocols. Amplification usually requires
the use of specific nucleic acid primers, to initiate the
reaction.
[0141] Nucleic acid primers useful for amplifying sequences from
the gene or locus are able to specifically hybridize with a portion
of the gene locus that flanks a target region of said locus, said
target region being altered in certain subjects having autism.
[0142] Preferred technique uses allele-specific PCR (AS-PCR). This
technique allows amplification to target specific alleles. AS-PCR
is performed with three primers including two primers with the same
nucleotide sequences except in 3' direction with one base
corresponding to the specific allele. Additionally, two universal
primers which are coupled to a specific fluorophore are used. These
two primers will transmit a signal if they are incorporated in a
PCR product (Nazarenko et al. 1997; Myakishev et al. 2001).
[0143] This technique can be performed in a single tube, in a
microplate and run in a classical qPCR system. But the new
platforms of micro-fluidic can also be used for running this
technique, with the advantage to interrogate in parallel several
ten of samples on several ten of markers.
[0144] As an example: The Fluidigm Dynamic Array as large as a
96-well plate allows a study of 96 SNP on 96 samples; therefore
9216 reactions of PCR are performed in parallel. The samples and
primers are distributed in reaction chambers of a few nanoliters by
a system of micro-fluidics.
[0145] Fluidigm Dynamic Array integrated fluidic circuits (IFCs)
have an on-chip network of microfluidic channels, chambers and
valves that automatically assemble individual PCR reactions,
decreasing the number of pipetting steps required by up to 100
fold. After loading the samples and primers onto the Dynamic
Arrays, the PCR is then performed on BioMark or EP1 System
integrating thermal cycling and fluorescences detection on
Integrated fluidic circuits. (Wang et al 2009(b))
[0146] Hybridization detection methods are based on the formation
of specific hybrids between complementary nucleic acid sequences
that serve to detect nucleic acid sequence alteration(s).
[0147] A particular detection technique involves the use of a
nucleic acid probe specific for wild type or altered gene, followed
by the detection of the presence of a hybrid. The probe may be in
suspension or immobilized on a substrate or support (as in nucleic
acid array or chips technologies). The probe is typically labelled
to facilitate detection of hybrids.
[0148] In a preferred embodiment, an alteration in the gene locus
is determined by DNA chip analysis. Such DNA chip or nucleic acid
microarray consists of different nucleic acid probes that are
chemically attached to a substrate, which can be a microchip, a
glass slide or a microsphere-sized bead. A microchip may be
constituted of polymers, plastics, resins, polysaccharides, silica
or silica-based materials, carbon, metals, inorganic glasses, or
nitrocellulose. Probes comprise nucleic acids such as cDNAs or
oligonucleotides that may be about 10 to about 60 base pairs. To
determine the alteration of the genes, a sample from a test subject
is labelled and contacted with the microarray in hybridization
conditions, leading to the formation of complexes between target
nucleic acids that are complementary to probe sequences attached to
the microarray surface. The presence of labelled hybridized
complexes is then detected. Many variants of the microarray
hybridization technology are available to the man skilled in the
art (see e.g. the review by Kidgell&Winzeler, 2005).
[0149] The invention further provides a kit comprising primers
pairs (forward and reverse primers) or triplets (two forward and
one reverse primers) and/or probes for the specific detection of a
SNP in the gene loci of at least HTR5A, MACF1, RBFOX1, ABR, PTPRG,
CACNA2D1, GFRA1, DSCAML1, CHRM3, LPPR4, DLG2, SLC9A9 and BASP1,
preferably the SNPs are rs893109 in HTR5A (position 27 of SEQ ID
NO: 31), rs260969 in MACF1 (position 27 on SEQ ID NO: 15),
rs12925135 in RBFOX1 (position 27 of SEQ ID NO: 39), rs2663327 in
ABR (position 27 of SEQ ID NO: 40), rs636624 in PTPRG (position 27
of SEQ ID NO: 22), rs2367910 in CACNA2D1 (position 27 of SEQ ID NO:
41), rs10787637 in GFRA1 (position 27 of SEQ ID NO: 4), rs695083 in
DSCAML1 (position 27 of SEQ ID NO: 24), rs10802802 in CHRM3
(position 27 of SEQ ID NO: 5), rs712886 in LPPR4 (position 27 of
SEQ ID NO: 27), rs12275631 in DLG2 (position 27 of SEQ ID NO: 51),
rs3928471 in SLC9A9 (position 27 of SEQ ID NO: 19), and rs298542 in
BASP1 (position 27 of SEQ ID NO: 52).
[0150] The kit may further comprise primers pairs (forward and
reverse primers) or triplets (two forward and one reverse primers)
and/or probes for the specific detection of a SNP in the gene loci
of any or all of KCNIP1, UGCG, NTRK3, PLCB1, NELL1, GPR98, MAGI2,
PLAGL1, CNTN6, DLG4, ERC2, TRIM9, SYT14, JARID2, CDH13, SULF2,
GRIN2A and NRG3, or combinations thereof, preferably the kit
further comprises primers pairs (forward and reverse primers) or
triplets (two forward and one reverse primers) and/or probes for
the specific detection of any or all of rs12514116 in KCNIP1
(position 27 on SEQ ID NO: 38), rs16916456 in UGCG (position 27 of
SEQ ID NO: 11), rs7172184 in NTRK3 (position 27 of SEQ ID NO: 28),
rs8123323 in PLCB1 (position 27 of SEQ ID NO: 37), rs10766739 in
NELL1 (position 27 of SEQ ID NO: 3), rs16868972 in GPR98 position
27 of SEQ ID NO: 42), rs12535987 in MAGI2 (position 27 of SEQ ID
NO: 43), rs207668 in PLAGL1 (position 27 of SEQ ID NO: 12),
rs9837484 in CNTN6 (position 27 of SEQ ID NO: 35), rs314253 in DLG4
(position 27 of SEQ ID NO: 17), rs1485677 in ERC2 (position 27 of
SEQ ID NO: 8), rs10150121 in TRIM9 (position 27 of SEQ ID NO: 1),
rs7534723 in SYT14 (position 27 of SEQ ID NO: 30), rs9370809 in
JARID2 (position 27 of SEQ ID NO: 33), rs9940922 in CDH13 (position
27 of SEQ ID NO: 36), rs6063144 in SULF2 (position 27 of SEQ ID NO:
53), rs4782109 in GRIN2A (position 27 of SEQ ID NO: 21), and
rs2820100 in NRG3 (position 27 of SEQ ID NO: 54), or combinations
thereof.
[0151] Said kit may also or in addition further comprises primers
pairs (forward and reverse primers) or triplets (two forward and
one reverse primers) and/or probes for the specific detection of a
SNP in the gene loci of any or all of NRG1, TRIM2, EPHA5, PCDH10,
HIP1, APBA1, PDE4D and EGLN3, or combinations thereof, preferably
the kit further comprises primers pairs (forward and reverse
primers) or triplets (two forward and one reverse primers) and/or
probes for the specific detection of any or all of rs723811 in NRG1
(position 27 on SEQ ID NO: 44), rs11139294 in APBA1 (position 27 of
SEQ ID NO: 6), rs11942354 in TRIM2 (position 27 of SEQ ID NO: 45),
rs1597611 in EPHA5 (position 27 of SEQ ID NO: 10), rs4404561 in
PCDH10 (position 27 of SEQ ID NO: 20), rs6962352 in HIP1 (position
27 of SEQ ID NO: 25), rs7075400 in NRG3 (position 27 of SEQ ID NO:
55), rs35284 in PDE4D (position 27 of SEQ ID NO: 18) and rs946630
in EGLN3 (position 27 of SEQ ID NO: 56), or combinations
thereof.
[0152] Said kit may also or in addition further comprises primers
pairs (forward and reverse primers) or triplets (two forward and
one reverse primers) and/or probes for the specific detection of at
least one SNP in the gene loci selected from the group consisting
of ABR, ACCN1, AKAP7, APBA1, ASTN2, BASP1, CACNA2D1, CADM1, CDH13,
CHRM3, CNTN6, DCLK1, DCLK2, DLG2, DLG4, DSCAML1, EGLN3, EPHA5,
ERC2, GFRA1, GPR98, GRIN2A, GRIN2B, GRM7, HIP1, HTR5A, JARID2,
KCNH5, KCNIP1, LPPR4, MACF1, MAGI2, MAP1S, MAP2K1, NAV2, NELL1,
NRG1, NRG3, NTRK3, PAX2, PCDH10, PDE11A, PDE4D, PLAGL1, PLCB1,
PTPRD, PTPRG, RBFOX1, RGS6, SLC24A2, SLC9A9, SULF2, SYT14, TRIM2,
TRIM9 and UGCG, or combinations thereof, preferably the kit further
comprises primers pairs (forward and reverse primers) or triplets
(two forward and one reverse primers) and/or probes for the
specific detection of any or all of rs2663327, rs7225320,
rs6923644, rs11139294, rs7021928, rs298542, rs2367910, rs220836,
rs9940922, rs10802802, rs9837484, rs1556060, rs9307866, rs12275631,
rs314253, rs695083, rs946630, rs1597611, rs1485677, rs10787637,
rs16868972, rs4782109, rs7974275, rs1569284, rs6962352, rs893109,
rs9370809, rs1041644, rs12514116, rs712886, rs260969, rs12535987,
rs12985015, rs1432443, rs10500866, rs10766739, rs723811, rs2820100,
rs7075400, rs7172184, rs2077642, rs4404561, rs2695112, rs35284,
rs2076683, rs8123323, rs2382104, rs636624, rs12925135, rs6574041,
rs957910, rs3928471, rs6063144, rs7534723, rs11942354, rs10150121,
rs16916456, or combinations thereof.
[0153] In particular, the kit may comprise primers pairs (forward
and reverse primers) or triplets (two forward and one reverse
primers) and/or probes for the specific detection of at least one
SNP in all of the following the gene loci: ABR, ACCN1, AKAP7,
APBA1, ASTN2, BASP1, CACNA2D1, CADM1, CDH13, CHRM3, CNTN6, DCLK1,
DCLK2, DLG2, DLG4, DSCAML1, EGLN3, EPHA5, ERC2, GFRA1, GPR98,
GRIN2A, GRIN2B, GRM7, HIP1, HTR5A, JARID2, KCNH5, KCNIP1, LPPR4,
MACF1, MAGI2, MAP1S, MAP2K1, NAV2, NELL1, NRG1, NRG3, NTRK3, PAX2,
PCDH10, PDE11A, PDE4D, PLAGL1, PLCB1, PTPRD, PTPRG, RBFOX1, RGS6,
SLC24A2, SLC9A9, SULF2, SYT14, TRIM2, TRIM9 and UGCG, preferably
the kit comprises primers pairs (forward and reverse primers) or
triplets (two forward and one reverse primers) and/or probes for
the specific detection of all following SNPs: rs2663327, rs7225320,
rs6923644, rs11139294, rs7021928, rs298542, rs2367910, rs220836,
rs9940922, rs10802802, rs9837484, rs1556060, rs9307866, rs12275631,
rs314253, rs695083, rs946630, rs1597611, rs1485677, rs10787637,
rs16868972, rs4782109, rs7974275, rs1569284, rs6962352, rs893109,
rs9370809, rs1041644, rs12514116, rs712886, rs260969, rs12535987,
rs12985015, rs1432443, rs10500866, rs10766739, rs723811, rs2820100,
rs7075400, rs7172184, rs2077642, rs4404561, rs2695112, rs35284,
rs2076683, rs8123323, rs2382104, rs636624, rs12925135, rs6574041,
rs957910, rs3928471, rs6063144, rs7534723, rs11942354, rs10150121,
rs16916456.
[0154] The kit may also further comprise primers pairs (forward and
reverse primers) or triplets (two forward and one reverse primers)
and/or probes for the specific detection of a SNP in the gene loci
of any or all of PITX1, ATP2B2, EN2, JARID2, MARK1, ITGB3, CNTNAP2,
and HOXA1, or combinations thereof, preferably the kit further
comprises primers pairs (forward and reverse primers) or triplets
(two forward and one reverse primers) and/or probes for the
specific detection of any or all of the SNP selected from the group
consisting rs6872664, rs2278556, rs1861972, rs7766973, rs12410279,
rs5918, rs7794745, and rs10951154, or combinations thereof. These
genes/SNPs are those described in Carayol et al, 2011 and
WO2011/138372.
[0155] Primer pairs (forward and reverse primers) or triplets (two
forward and one reverse primers) may be used for specific
amplification of part of a target gene comprising the SNP of
interest. When only two primers are used, they are generally
located each on one side of the target SNP of interest and are used
in order to increase the amount of target sequence for further
analysis. When three primers are used, the single reverse primer is
preferably located on one side of the target SNP of interest, while
the two corresponding forward primers are respectively specific of
the protective or risk-associated allele of the SNP. The base
differing between the two primers is preferably located in 3' of
the forward primers. Primers are polynucleotides of about 15 to
about 25 nucleotides, preferably of about 18 to about 22
nucleotides.
[0156] A probe for the specific detection of a SNP in a gene locus
may notably comprise or consist of a polynucleotide comprising at
least 10 contiguous bases, preferably about 10 to about 60 bases,
complementary to part of a target gene comprising the SNP of
interest.
[0157] In particular, the invention provides a set of
polynucleotides comprising at least 10 contiguous bases, preferably
about 10 to about 60 bases, of (i) SEQ ID NO: 31, 15, 39, 40, 22,
41, 4, 24, 5, 27, 51, 19 and 52 respectively around position 27 of
SEQ ID NO: 31, position 27 of SEQ ID NO:15, position 27 of SEQ ID
NO: 39, position 27 of SEQ ID NO: 40, position 27 of SEQ ID NO: 22,
position 27 of SEQ ID NO: 41, position 27 of SEQ ID NO: 4, position
27 of SEQ ID NO: 24, position 27 of SEQ ID NO:5, position 27 of SEQ
ID NO: 27, position 27 of SEQ ID NO: 51 and position 27 of SEQ ID
NO: 19, or (ii) of the complement of said sequences. Such a set of
polynucleotides may further comprise polynucleotides comprising at
least 10 contiguous bases, preferably about 10 to about 60 bases,
of (i) SEQ ID NO:38, SEQ ID NO:11, SEQ ID NO:28, SEQ ID NO:37, SEQ
ID NO:3, SEQ ID NO:42, SEQ ID NO:43, SEQ ID NO:12, SEQ ID NO:35,
SEQ ID NO:17, SEQ ID NO:8, SEQ ID NO:1, SEQ ID NO:30, SEQ ID NO:33,
SEQ ID NO:36, SEQ ID NO:53, SEQ ID NO:21, SEQ ID NO:54 and SEQ ID
NO:55, respectively around positions of SEQ ID NO:38, SEQ ID NO:11,
SEQ ID NO:28, SEQ ID NO:37, SEQ ID NO:3, SEQ ID NO:42, SEQ ID
NO:43, SEQ ID NO:12, SEQ ID NO:35, SEQ ID NO:17, SEQ ID NO:8, SEQ
ID NO:1, SEQ ID NO:30, SEQ ID NO:33, SEQ ID NO:36, SEQ ID NO:53,
SEQ ID NO:21, SEQ ID NO:54 and SEQ ID NO:55 mentioned in Table 1,
or (ii) of the complement of said sequences. Such a set of
polynucleotides may further comprise polynucleotides comprising at
least 10 contiguous bases, preferably about 10 to about 60 bases,
of (i) SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:10, SEQ ID NO:20, SEQ
ID NO:25, SEQ ID NO:6, SEQ ID NO:18 and SEQ ID NO:56, respectively
around positions of SEQ ID NO:44, SEQ ID NO:45, SEQ ID NO:10, SEQ
ID NO:20, SEQ ID NO:25, SEQ ID NO:6, SEQ ID NO:18 and SEQ ID NO:56
mentioned in Table 1, or (ii) of the complement of said
sequences.
[0158] In a preferred embodiment, the invention provides a set of
polynucleotides comprising at least 10 contiguous bases, preferably
about 10 to about 60 bases, of (i) each of SEQ ID NO:1 to SEQ ID
NO:57, respectively around positions of SEQ ID NO:1 to SEQ ID NO:57
mentioned in Table 1, or (ii) of the complement of said
sequences.
[0159] The above sets of polynucleotides may further comprise at
least 10 contiguous bases, preferably about 10 to about 60 bases,
of (i) each of SEQ ID NO:58 to SEQ ID NO:65, respectively around
positions of SEQ ID NO:58 to SEQ ID NO:65 mentioned in Table 2, or
(ii) of the complement of said sequences.
[0160] Preferably, the kit according to the invention is dedicated
to the genotyping of the target SNPs of interest. By "dedicated",
it is meant that primer pairs (forward and reverse primers) or
triplets (two forward and one reverse primers) and/or probes for
the specific detection of a SNP in the kit of the invention
essentially consist of those necessary to the specific detection of
the SNPs of interest, and thus comprise a minimum of primer pairs
(forward and reverse primers) or triplets (two forward and one
reverse primers) and/or probes for the specific detection of other
SNPs than those mentioned above. For instance, a dedicated kit of
the invention preferably comprises no more than 50, 40, 30, 25, 20,
preferably no more than 15, no more than 14, no more than 13, no
more than 12, no more than 11, preferably no more than 10,
preferably no more than 9, 8, 7, 6, 5, 4, 3, 2, or 1 primer pairs
(forward and reverse primers) or triplets (two forward and one
reverse primers) and/or probes for the specific detection of other
SNPs than those mentioned above. The dedicated kit of the invention
thus preferably contains no more than 100, 90, 80, preferably no
more than 70, no more than 69, no more than 68, no more than 67, no
more than 66, preferably no more than 65 distinct primer pairs
(forward and reverse primers) or triplets (two forward and one
reverse primers) and/or probes for the specific detection of SNPs.
It may however contain additional reagents such as a polymerase,
buffers or any other useful reagent. It may further contain
instructions for determining a risk of autism, a predisposition to
autism or the presence of autism. For instance, it may contain
instructions for calculating a genetic score and appropriate
threshold value(s).
[0161] A further subject of the invention is a microarray
comprising a set of polynucleotides and optionally, a substrate on
which the set of polynucleotides is immobilized, wherein the set of
polynucleotides is as defined above. Such a microarray is also
preferably dedicated the genotyping of the target SNPs of interest.
For a microarray, this means that the specific probes of the
microarray essentially consist of probes specific for the target
SNPs of interest and only comprise a minimum of probes specific for
other SNPs. Preferably, a dedicated microarray comprises no more
than 50, 40, 30, 25, 20, preferably no more than 15, no more than
14, no more than 13, no more than 12, no more than 11, preferably
no more than 10, preferably no more than 9, 8, 7, 6, 5, 4, 3, 2, or
1 probes for the specific detection of other SNPs than those
mentioned above. The dedicated microarray of the invention thus
preferably contains no more than 100, 90, 80, preferably no more
than 70, no more than 69, no more than 68, no more than 67, no more
than 66, preferably no more than 65 distinct probes for the
specific detection of SNPs.
[0162] Preferably the polynucleotides are immobilized on a
substrate coated with an active group selected from the group
consisting of amino-silane, poly-L-lysine and aldehyde.
[0163] In a particular embodiment, the substrate is composed of a
material selected from the group consisting of silicon, glass,
quartz, metal and plastic.
Methods of Treatment
[0164] The present invention further relates to methods for
treating or preventing autism in a subject, the method comprising:
[0165] a) determining a risk of autism, or detecting predisposition
to or the presence of autism in a subject by any method according
to the invention described herein, and [0166] b) if said subject is
determined to be at risk of autism, as predisposed to autism or as
suffering from autism, then submitting said subject to: [0167] i) a
behavioral autism instrument, such as Autism Diagnostic Observation
Schedule-Generic [ADOS-G], [0168] ii) an indirect, interview-based
autism instrument with third parties, such as Autism Diagnostic
Interview-Revised [ADI-R], and/or [0169] iii) Early Intensive
Behavioural Intervention (EIBI).
[0170] Preferably, if the subject is determined to be at risk of
autism, as predisposed to autism or as suffering from autism, then
said subject is first rapidly submitted to a clinical evaluation,
including behavioral or an indirect, interview-based autism
instrument, preferably the Autism Diagnostic Interview-Revised
[ADI-R] test in order to confirm the diagnosis of autism. If autism
diagnosis is confirmed, then the subject is rapidly submitted to
Early Intensive Behavioural Intervention (EIBI), since early
intervention has been found to improve outcome for autistic
subjects.
[0171] The methods of determining a risk of autism, or of detecting
the predisposition to or the presence of autism in a subject
according to the invention are mainly intended for screening young
and very young children for autism, in particular young brothers or
sisters of a child already diagnosed as suffering from autism, as
early as possible, even before behavioral autism tests (e.g. Autism
Diagnostic Observation Schedule-Generic [ADOS-G]) or indirect,
interview-based autism tests with third parties (e.g., Autism
Diagnostic Interview-Revised [ADI-R]) may be performed. This may
permit to perform such tests and confirm autism as early as
possible, thus allowing early therapeutic intervention.
[0172] Indeed, the American Academy of Pediatrics (AAP) has
published clinical practice guidelines on the early identification,
screening and diagnosis of ASD with recommendations that all 18-
and 24-months olds be screened for ASD (Johnson and Myers
2007).
[0173] If the screening result is positive, the pediatrician should
provide peer reviewed and/or consensus-developed ASD materials.
Because a positive screening result does not determine a diagnosis
of ASD, the child should be referred for a comprehensive ASD
evaluation, to early intervention/early childhood education
services, and an audiologic evaluation (Johnson and Myers
2007).
[0174] There is some evidence that Early Intensive Behavioural
Intervention (EIBI)--incorporating the principles of applied
behavior analysis (ABA)--is an effective intervention approaches
for young children with autism (Dawson and Osterling 1997; Warren
et al. 2011; Reichow et al. 2012). However, the current state of
the evidence is limited due to the lack of randomized controlled
trials.
[0175] The only comprehensive EIBI program available for children
aged less than 30 months that has been empirically evaluated is the
Early Start Denver Model (ESDM) (Dawson et al. 2010). After 2 years
of intensive intervention, compared with children who received
community-intervention, children who received the ESDM displayed
significantly improved IQ with an increased of 17.6 points compared
with 7.0 points in the comparison group relative to baseline
scores. Children in the comparative group showed greater delays in
adaptive behavior. Although, children who received ESDM were more
likely to experience a change in diagnosis from autism to pervasive
developmental disorder not otherwise specified, than the comparison
group. Moreover, the authors demonstrated EIBI was associated with
normalized patterns of brain activity, which is associated with
improvements in social behavior, in young children with autism
spectrum disorder (Dawson et al. 2012).
[0176] The lifetime per capita incremental societal cost of autism
has been evaluated to $2.2 million (Ganz 2007). In a cost-benefit
study of EIBI, Jacobsen and al. estimated the net savings to age 55
for a child with PDD who achieves normal functioning is $1.5
million and the net savings for the child who achieves partial
effects is roughly $1 million (Jacobson and Mulick 2000).
[0177] Therefore, early screening, followed by early confirmative
diagnosis and/or intervention could be very helpful to improve the
fate of autistic subject and decrease the cost associated to their
management.
[0178] The test according to the invention may thus be used for
early screening, and followed by confirmative diagnosis and/or
intervention if a risk of autism, a predisposition to autism or the
presence of autism is diagnosed.
[0179] In this case, the confirmative diagnosis may be made using
behavioral autism diagnosis instruments (e.g. Autism Diagnostic
Observation Schedule-Generic [ADOS-G]) (Gotham et al. 2007) or
indirect, interview-based autism diagnosis instruments (e.g.,
Autism Diagnostic Interview-Revised [ADI-R]) (Lord et al, 1994).
Such tests are well known to those skilled in the art.
[0180] If autism is confirmed, or even before such confirmative
diagnosis may be performed, therapeutic intervention may be
performed. There are no evidence-based pharmacotherapies to treat
the core symptoms associated with ASD but, as mentioned above,
there is some evidence that Early Intensive Behavioural
Intervention (EIBI)--incorporating the principles of applied
behavior analysis (ABA)--is an effective intervention approaches
for young children with autism.
[0181] Early Intensive Behavioral Intervention (EIBI) is one of the
more well-established treatments for ASD. EIBI is a highly
structured teaching approach for young children with
ASD (usually less than five years old), that is rooted in
principles of applied behavior analysis (ABA). The origins of EIBI
are linked to the University of California at Los Angeles Young
Autism Project model (also termed the Lovaas model) (see Lovaas
1981 and Lovaas 1987). The core elements of EIBI involve (a) a
specific teaching procedure referred to as discrete trial training,
(b) the use of a 1:1 adult-to-child ratio in the early stages of
the treatment, and (c) implementation in either home or school
settings for a range of 20 to 40 hours per week across one to four
years of the child's life (see Eikeseth 2009 and Smith 2010).
Typically, EIBI is implemented under the supervision of personnel
trained in ABA procedures who systematically follow a treatment
manual (for example, Lovaas 1981; Maurice 1996) indicating the
scope and sequence of tasks to be introduced and taught. A
particular example of EIBI is the Early Start Denver Model (ESDM),
which is described in Smith et al, 2008.
[0182] The following examples illustrate the present invention
without limiting its scope.
Examples
1. Materials and Methods
Subjects and Genotyping
[0183] Two independent sets of autism multiplex family samples were
used. The discovery population consisted of 545 multiplex families
from the AGRE repository (Lajonchere et al, 2010), including 964
affected siblings (773 males and 191 females; 4.1:1 male to female
sex ratio) and 317 unaffected siblings (144 males and 173 females).
The validation population consisted of 288 multiplex families from
a totally independent collection enriched with a complimentary set
of 339 families from AGRE. It was composed by 1 000 affected
siblings (812 males and 188 females; 4.3:1 male to female sex
ratio) and 288 unaffected siblings (141 males and 147 females).
Detailed diagnostic criteria for the AGRE data set can be found on
the AGRE website (http://www.agre.org/). Only individuals with a
"strict" definition of autism according to the Autism Diagnostic
Interview Revisited (ADI-R) were selected to improve the power of
GWAS by homogenizing the phenotype (Shao et al, 2002 and McCarthy
et al, 2008). Members of the AGRE families were genotyped as
previously described (Wang et al, 2009). SNPs that failed Hardy
Weinberg Equilibrium Test (P<10.sup.-3) or that have a call rate
less than 90% or a minor allele frequency less than 5% were
removed. Mendelian transmissions of alleles were checked for every
SNP and genotypes that were inconsistent with Mendelian inheritance
in one or several families were set to unknown in all the members
of the families showing the error. SNPs identified in the discovery
population were genotyped in the validation collection as
previously described (Carayol et al, 2011).
Association Studies
[0184] GWAS were performed using the Family Based Association Test
(FBAT) software (Laird et al, 2000) under additive and
recessive/dominant (in both possible orientations: major allele
dominant/minor allele dominant) inheritance models. SNPs with a
p-value less than 10.sup.-3 were tested for their ability to
discriminate individuals with autism from their unaffected
siblings. A "case-sibling control" association analysis was
performed and odds ratio were estimated using a Generalized
Estimating Equation (GEE) model to account for the non-independence
of individuals from the same family (Zeger et al, 1986). The gender
was introduced as an adjustment covariate when it was not used as
variable of stratification. Markers associated at the nominal
threshold (.alpha.=0.05) were selected for subsequent analyses.
SNP Prioritization
[0185] To extract association signals from the GWAS and to minimize
false positive SNPs, the inventors have developed a scoring method
where points were allotted to statistical parameters, genomic
characteristics, previous reporting and physiological properties
for each selected SNP and its related gene(s).
Definition of the Genetic Models and Development of Genetic
Scores
[0186] Replicability of the genetic models was tested for each SNP
internally and externally using bootstrap resampling in the
discovery and the validation populations by computing a
Reproducibility Index (RI) (Carayol et al, 2011 and Carayol et al,
2010 and Ma et al, 2006). Reproducibility Index was computed as
previously described (Carayol et al, 2011):
1. Generation of a `pseudo-sample` consisting of 545 families by
randomly sampling the 545 families of the discovery population with
replacement. 2. For each tested SNP i, odds ratio (OR) associated
with the deleterious allele under additive (OR.sub.Add,i),
recessive (OR.sub.Rec,i) and dominant (OR.sub.DOM,i) models were
estimated. 3. Steps 1 and 2 were repeated a 1 000 times. 4. For
each tested SNP i, computation of M.sub.GM,i which represents the
number of times the deleterious allele maintains its deleterious
effect under each genetic model (GM=additive, recessive, dominant),
i.e. the number of times OR.sub.GM,i>1.00 in the thousand
pseudos-amples in males and females separately. 5. Three RIs for
each SNP i, one for each genetic model (GM), were calculated as
RI.sub.GM,i=M.sub.GM,i/1 000 in males and females separately. 6.
Repetition of steps 1 to 5 using the validation population.
[0187] SNPs were included in genetic scores based on their degree
of reproducibility. Considering a stringent RI threshold of 90%, a
SNP under a specific genetic model was included in the genetic
score (GS90%) if the estimated RI of this model was greater than
90% in both the discovery and validation populations. This highly
reproducible model is considered to be the "best-fitting model". In
case more than one model fulfilled this criterion, the model with
the highest RI estimated in the validation population was selected.
Subsequent genetic score models (GS80% to GS0%) were constructed by
adding to the previous set of SNPs new genetic markers under their
best-fitting genetic model using relaxed RI thresholds from 80% to
0%. Genetic scores of individuals with autism and their unaffected
siblings from the validation population were built as the sum of
deleterious alleles under their specific genetic model, as
previously described (Carayol et al, 2011). GEE model was used to
test the association of the genetic scores with autism and a
p-value less than 5% indicated a significant association. Areas
under the receiver operating characteristic curve (AUCs) which
quantify the ability of the genetic scores to discriminate affected
from unaffected individuals were estimated using the "ROC" R
package (www.bioconductor.org/packages/devel/bioc/html/ROC.html).
Empirical 95% confidence intervals (CIs) were determined by
bootstrapping 1 000 times the validation sample using each family
as a resampling unit. Positive predictive values were estimated as
previously described (Carayol et al, 2011) using sensitivity,
specificity and sibling recurrence risk of 25.9% in males and 9.6%
in females (Ozonoff et al., 2011).
2. Results
[0188] Four family-based GWAS were performed on the discovery
population of multiplex autism families, three on affected
individuals with and without gender stratification and one on their
unaffected siblings. In total, 900 SNPs were found to be associated
with autism (p-value <10.sup.-3 in family-based GWAS) and to
significantly discriminate affected from unaffected siblings
(p-value <0.05 in "case-sibling control" analysis).
Specifically, 149 and 237 SNPs were identified through the GWAS
conducted on autistic males and females, respectively, 156 when all
the affected individuals were analyzed, and 358 from the GWAS on
unaffected siblings. Prioritization of these 900 SNPs identifies
133 candidate genetic markers of autism.
Construction of Genetic Scores (GS)
[0189] Gender-specific genetic scores (GS) for affected and
unaffected siblings were built using SNPs under their best-fitting
genetic model selected depending on the RI threshold considered.
The ability of the genetic score models to discriminate affected
from unaffected individuals, indicated by the AUC, and their
association with autism was assessed for the different genetic
score models. Genetic scores built using all the identified SNPs
(GS0%) were significantly associated with autism in males
(P=1.16.times.10.sup.-3) and females (P=5.97.times.10.sup.-5) with
AUCs of 0.59 (95% CI: 0.54-0.65) and 0.65 (95% CI: 0.58-0.72),
respectively. AUC estimates increased along with genetic score
models and reach their maximum, 0.73 (95% CI: 0.69-0.78) in males
and 0.74 (95% CI: 0.68-0.80) in females, for GS60% in males and
GS40% in females. A slight decrease of the AUCs to 68% (95% CI:
0.63-0.73) was observed from GS60% to the more stringent GS90% in
males whereas AUC estimates remained close to 74% in females from
GS40% to GS90%.
Reliability of GS80 in Males and Females with Varying Number of
Threshold Values GS80 with Only One Threshold Value
[0190] Considering a stringent RI threshold of 80% as previously
described in Carayol et al (2011) (GS80%), 57 SNPs (see Table 1)
were distributed in two gender-specific clusters of 31 SNPs (5 SNPs
were present in the two clusters, see Tables 5 and 6) with an
average RI of 92% and 93% in the discovery and validation samples,
respectively. AUCs for this genetic model were similar in both the
discovery (0.70, 95% CI: 0.65-0.75, for males and 0.76, 95% CI:
0.71-0.81, for females) and the validation populations (0.70, 95%
CI: 0.65-0.74, for males and 0.73, 95% CI: 0.67-0.79, for females)
indicating that this model was stable, i.e. it has the same
discriminative ability in two independent populations.
[0191] If such an additive default model is assumed for all 31 SNPs
in males genetic score and 31 SNPs in female genetic score, AUC are
estimated to 0.68 (p=4.9.times.10.sup.-11) and 0.72
(p=5.1.times.10-12) in males and females respectively in the
discovery population and 0.65 (p=1.5.times.10-7) in males and 0.64
(p=3.09.times.10-5) in females in the validation population,
showing that lower but significant performance is obtained using a
simple additive default model.
[0192] Genetic scores of the affected individuals and their
unaffected siblings in the validation population ranged from 22 to
48 in males, and from 22 to 45 in females. To evaluate the
discriminative performance of the genetic score model GS80%,
specificity, sensitivity, and positive predictive values (PPV) were
estimated for different genetic score thresholds (see Tables 7 and
8).
[0193] In males, assuming a 25.9% prevalence in males siblings of
children with autism, any threshold value above 27 (GS.gtoreq.27)
is associated to a significant increase in risk or positive
predictive value (95% confidence intervals did not include 25.9% in
Table 7) and could be used as threshold value. If one want to limit
the number of false positive to 20% (i.e. high specificity), a
threshold value of 37 or higher allows to identify children with a
risk of autism (i.e. positive predictive value) of 43.7% (GS=37
threshold value) or higher (any GS threshold value above 37).
[0194] At a threshold of 37 points, the model identified half of
the affected individuals (sensitivity=48%, 95% CI: 44%-51%) while
limiting the number of false positives to 21% (specificity=79%, 95%
CI: 70%-86%). Using a higher genetic score threshold of 40 points
dramatically decreased the number of false positives to 7%
(specificity=93%, 95% CI: 88%-98%) and identified 20% of affected
children (sensitivity=20%, 95% CI: 17%-23%). This genetic score
threshold was associated with a PPV of 51% (95% CI: 38%-73%) which
was twice as high as the reported 25.9% male sibling recurrence
risk (Ozonoff et al, 2011). Further values of sensitivity,
specificity and PPV for other single threshold values are provided
in following Table 7 for the GS80 in males in the validation
population.
TABLE-US-00007 TABLE 7 Discriminative performance of the genetic
score model GS80% in males from the validation population Genetic
score Sensi- Speci- Positive thresh- tivity ficity Predictive olds
(95% CI) (95% CI) Value (95% CI) 22 100% (--) 0% (--) 25.9% (--) 23
100% (99-100) 0% (--) 25.9% (--) 24 100% (99-100) 1% (0-3) 26.0%
(25.8-26.4) 25 100% (99-100) 2% (0-4) 26.1% (25.8-26.7) 26 99%
(98-100) 4% (0-8) 26.5% (25.8-27) 27 99% (98-100) 5% (2-9) 26.6%
(26.0-27.3) 28 98% (97-99) 8% (4-14) 27.2% (26.2-28.3) 29 97%
(95-98) 14% (8-20) 28.2% (26.9-29.8) 30 95% (93-97) 15% (9-22)
28.1% (26.7-29.7) 31 91% (89-93) 22% (15-30) 29.1% (27.3-31.3) 32
87% (85-90) 33% (25-42) 31.3% (28.9-34.8) 33 81% (78-84) 45%
(35-54) 33.9% (30.6-38.1) 34 74% (71-78) 55% (45-64) 36.4%
(32.2-41.7) 35 67% (64-71) 66% (57-75) 41.0% (35.2-47.8) 36 58%
(54-61) 73% (64-81) 42.4% (35.9-50.7) 37 48% (44-51) 79% (70-86)
43.7% (36.8-53.7) 38 39% (35-43) 84% (77-90) 45.2% (36.8-57.7) 39
27% (24-31) 90% (84-95) 49.1% (38.0-65.6) 40 20% (17-23) 93%
(88-98) 51.3% (37.8-73.1) 41 14% (11-17) 94% (90-98) 46.2%
(32.9-72.0) 42 10% (7-12) 97% (93-100) 50.0% (33.3-100.0.0) 43 6%
(4-8) 100% (--) 100% (--) 44 3% (2-4) 100% (--) 100% (--) 45 1%
(1-2) 100% (--) 100% (--) 46 1% (0-1) 100% (--) 100% (--) 47 1%
(0-1) 100% (--) 100% (--) 48 0% (--) 100% (--) 100% (--)
[0195] Following the same reasoning as for male, assuming a 9.6%
prevalence in female siblings of children with autism, any
threshold value above 24 (GS.gtoreq.24) is associated to a
significant increase in risk or positive predictive value (95%
confidence intervals did not include 9.6% in Table 8) and could be
used a threshold value. If one want to limit the number of false
positive to 20% (i.e. high specificity), a threshold value of 36 or
higher allow to identify children with a risk of autism (i.e.
positive predictive value) of 23.7% (GS=36 threshold value) or
higher (any GS threshold value above 36).
[0196] More than half of affected female individuals
(sensitivity=52%, 95% CI: 45%-60%) had a genetic score higher than
36 points whereas less than 20% of unaffected individuals exceeded
this threshold (specificity=82%, 95% CI: 76%-88%). A PPV of 27%
(95% CI: 19%-42%), which represented almost three times the
reported female sibling recurrence risk of 9.6% (Ozonoff et al,
2011), was reached at a genetic score threshold of 37 points and
was associated with a sensitivity of 39% (95% CI: 32%-47%) and a
specificity of 89% (95% CI: 83%-94%). Further values of
sensitivity, specificity and PPV for other single threshold values
are provided in following Table 8 for the GS80 in females in the
validation population.
TABLE-US-00008 TABLE 8 Discriminative performance of the genetic
score model GS80% in females from the validation population Genetic
score Sensi- Speci- Positive thresh- tivity ficity Predictive olds
(95% CI) (95% CI) Value (95% CI) 22 100% (--) 0% (--) 9.6% (--) 23
100% (--) 1% (0-2) 9.7% (9.6-9.8) 24 100% (--) 2% (0-4) 9.7%
(9.7-9.9) 25 99% (98-100) 2% (0-4) 9.7% (9.8-9.8) 26 99% (98-100)
3% (1-6 ) 9.8% (9.6-10.1) 27 99% (97-100) 5% (2-9) 10.0%
(10.7-10.3) 28 99% (97-100) 7% (3-12) 10.2% (9.8-10.6) 29 97%
(94-99) 17% (11-23) 11.1% (10.3-11.9) 30 94% (90-98) 27% (19-34)
12.0% (10.9-13.2) 31 90% (85-95) 36% (27-44) 12.9% (11.5-14.7) 32
82% (76-88) 42% (33-51) 13.1% (11.3-15.3) 33 75% (68-81) 54%
(45-63) 14.8% (12.5-17.8) 34 67% (60-74) 65% (57-73) 16.9%
(14.0-21.2) 35 58% (50-66) 70% (62-78) 17.2% (13.7-22.2) 36 52%
(45-60) 82% (76-88) 23.8% (17.6-33.0) 37 39% (32-47) 89% (83-94)
27.1% (18.6-41.6) 38 28% (21-35) 96% (92-99) 40.0% (24.8-67.3) 39
20% (14-27) 99% (65-100) 74.5% (45.7-100.0) 40 15% (10-21) 100%
(--) 100% (--) 41 10% (5-15) 100% (--) 100% (--) 42 7% (3-11) 100%
(--) 100% (--) 43 5% (2-8) 100% (--) 100% (--) 44 2% (1-5) 100%
(--) 100% (--) 45 1% (0-2) 100% (--) 100% (--)
[0197] To ascertain that the genetic score models were not
associated with autism by chance, male genetic score models were
applied to females and vice versa. In this configuration, no
association was observed and AUCs were not significantly different
from noninformativity (AUC=0.5). Specifically, assuming the same
stringent RI threshold of 80%, AUCs were estimated to be 0.47
(P=0.30) and 0.47 (P=0.34) in males and females, respectively.
GS80 with Several Threshold Values
[0198] A multi-risk class test may be constructed using more than
one threshold value. Two threshold values may be set to create 3
classes of risk: a reference class where the risk is close or equal
to the prevalence of the disease, a low risk class where the risk
is lower than the risk in the reference class, and a high risk
class where the risk is higher than in the reference class.
[0199] Example in females with two threshold values:
[0200] Using two threshold values in females (GS=32 and 37), three
classes are delineated: a first class defined as the reference
class (GS<37 and GS.gtoreq.32) where the risk is similar to the
prevalence in siblings 9.6%; a second class of lower risk
(GS<32) where the probability to be affected when the GS is
lower than 32 is 3%; and a third risk class of higher risk class
(GS.gtoreq.37), where the probability to be affected when the GS is
higher than 37 is 27%.
[0201] Then 3, 4 or more threshold values can also be applied.
[0202] Example in males with 4 threshold values:
[0203] Five classes are delineated using 4 GS threshold values (30,
35, 40 and 45): a reference class (GS.gtoreq.35 and GS<40) where
the risk is close to the prevalence of the disease; a high risk
class (GS.gtoreq.40 and GS<45), where the risk is 49% and a very
high risk class where the risk is 100%; a low risk class
(GS.gtoreq.30 and GS<35) were the risk is 16% and a very low
risk class (GS<30) were the risk is 8%.
[0204] The number and the value of the different threshold values
are settled according to the performance and characteristics
expected for the test defined by risk in classes, sensitivity and
specificity.
Further Genetic Scores (GS85, GS90, GS95)
[0205] Subgroups were then defined according to RI values for the
best fitted genetic model in the Discovery and Validation sample.
SNPs with a RI for a given genetic model greater than a fixed value
in both samples were selected to build the genetic score. The
process was applied in males and in females separately to construct
two different genetic score, one in males and one in females. Three
different RI value defining three different degrees of
reproducibility of the SNPs have been chosen: 0.95, 0.90 and 0.85.
AUCs and associated p-value have been provided for the different
genetic scores in males (Table 5) and females (Table 6).
GS80 Outperforms the Test Based on Genotyping of 4 or 8 SNPs
Previously Described in Carayol et al, 2010 and Carayol et al,
2011, Respectively, and May be Combined with this Test for Improved
Reliability
[0206] 4 and 8 SNPs genetic score models are proposed in Carayol et
al. (2010) and Carayol et al. (2011) respectively. The area under
the curve (AUC) is equal to the probability that a genetic score
will rank a randomly chosen affected patient higher than a randomly
chosen unaffected individual. AUCs were estimated to 0.59 (no
gender difference) for the 4 SNPs genetic score (Carayol et al.
2010) and, 0.59 and 0.66 in males and females respectively for a 8
SNPs gender specific genetic model (Carayol et al. 2011). Using 57
SNPs, AUCs increased to 0.7 in males and 0.73 in females in the
validation population. Despite the unambiguous interest and good
performances of the previously described tests based on genotyping
of 4 and 8 SNPs, the new test according to the invention, based on
analysis of 57 SNPs, is even more reliable.
[0207] In addition, the new test according to the invention may be
combined with the previously described test based on genotyping of
8 SNPs, resulting in further slightly improved reliability.
[0208] Tables 9 and 10 provide sensitivity, specificity as positive
and negative predictive value for a 65 SNPs gender specific genetic
score model in males and females. In males, the prevalence of
autism in siblings of affected children is estimated to 25.9%.
Using a genetic score threshold of 46 allow to identified 40% of
siblings (sensitivity) with a two-fold increase in risk (47.9%
positive predictive value) with only 15% of false positive results
(1 minus the specificity). The prevalence in female is estimated to
9.6%. Use of a 42 genetic score threshold identify 65% of siblings
(sensitivity) with a two-fold increased risk (22.5%) and less than
25% (1 minus specificity) false positive results. With a 48 genetic
score threshold, 14% of siblings (sensitivity) with more than 50%
risk (positive predictive value) are assessed and only 3% of false
positive results expected (1 minus specificity).
TABLE-US-00009 TABLE 9 Sensitivity, specificity as positive and
negative predictive value for a 37 SNPs genetic score model in
males Genetic Score Positive Negative Thresh- Sensi- Speci-
Predictive Predictive old tivity ficity Value Value 30 100.0% 0.0%
25.9% 100.0% 31 100.0% 0.8% 26.0% 100.0% 32 100.0% 1.5% 26.2%
100.0% 33 99.9% 2.3% 26.3% 97.9% 34 99.2% 3.0% 26.3% 91.0% 35 98.7%
5.3% 26.7% 92.2% 36 97.7% 9.8% 27.5% 92.5% 37 96.6% 15.8% 28.6%
93.0% 38 93.8% 21.8% 29.5% 91.0% 39 90.8% 30.8% 31.5% 90.6% 40
85.3% 36.8% 32.1% 87.8% 41 79.4% 45.9% 33.9% 86.4% 42 72.8% 54.9%
36.1% 85.2% 43 64.6% 63.9% 38.5% 83.8% 44 57.7% 71.4% 41.4% 82.8%
45 49.1% 78.9% 44.9% 81.6% 46 39.6% 85.0% 47.9% 80.1% 47 32.3%
90.2% 53.6% 79.2% .gtoreq.48 25.8% 96.2% 70.6% 78.8%
TABLE-US-00010 TABLE 10 Sensitivity, specificity as positive and
negative predictive value for a 36 SNPs genetic score model in
females Genetic Score Positive Negative Thresh- Sensi- Speci-
Predictive Predictive old tivity ficity Value Value 29 100.0% 0.0%
9.6% 100.0% 30 100.0% 1.3% 9.7% 100.0% 31 100.0% 2.5% 9.8% 100.0%
32 100.0% 3.1% 9.9% 100.0% 33 100.0% 4.4% 10.0% 100.0% 34 99.4%
6.3% 10.1% 99.1% 35 97.8% 11.3% 10.5% 98.0% 36 95.6% 20.1% 11.3%
97.7% 37 93.4% 30.8% 12.5% 97.8% 38 91.2% 36.5% 13.2% 97.5% 39
86.7% 44.7% 14.3% 96.9% 40 81.2% 56.6% 16.6% 96.6% 41 73.5% 67.9%
19.6% 96.0% 42 65.2% 76.1% 22.5% 95.4% 43 54.1% 82.4% 24.6% 94.4%
44 44.8% 84.9% 23.9% 93.5% 45 33.7% 89.3% 25.1% 92.7% 46 24.9%
93.1% 27.6% 92.1% 47 16.0% 97.5% 40.3% 91.6% 48 13.8% 98.7% 53.8%
91.5% 49 10.5% 99.4% 63.9% 91.3% .gtoreq.50 7.2% 100.0% 100.0%
91.0%
A Particular SNP of Interest May be Replaced by Another SNP in
Linkage Disequilibrium with this SNP of Interest
[0209] SNP rs7172184 belongs to the genetic score in males and
females. If this SNP is replaced by rs2018052, a SNP in linkage
disequilibrium (r2=0.815 as defined in HapMap), based on the
discovery population, the AUCs are estimated to 0.70
(p=6.06.times.10.sup.-14) in males and 0.75
(p=5.7.times.10.sup.-16) in females instead of 0.70 and 0.76 with
the original SNP (rs7172184).
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Sequence CWU 1
1
65152DNAArtificial SequenceContext sequence of SNP rs10150121
1ctgtgctact taatgtagac cacctgyttg atttcctgaa tgtggtctat gt
52252DNAArtificial SequenceContext sequence of SNP rs1041644
2caccatttta aagagtttaa ctaaatmaaa ttcatcaatg tttccactat gt
52352DNAArtificial SequenceContext sequence of SNP rs10766739
3acacaattgg caaaaccccc tgtcacrgtc agtaaacttt gaggacctgc tc
52452DNAArtificial SequenceContext sequence of SNP rs10787637
4tggatagttg gactctgcaa cctactraaa caaacatgtt aaaaattaaa ca
52552DNAArtificial SequenceContext sequence of SNP rs10802802
5tctgtctcat ccctggtcag gatgtargta taagtttgaa ggatcaaaaa at
52652DNAArtificial SequenceContext sequence of SNP rs11139294
6tgccctcatg gaacttaccc tgaggarttt acaatagaaa taattaaaca ta
52752DNAArtificial SequenceContext sequence of SNP rs12985015
7cagcagtcct gagggctcag ggttccrttt tccccacaaa tgccatcatc tg
52852DNAArtificial SequenceContext sequence of SNP rs1485677
8ttttaattaa tcctctgcaa ggaaccragt tttgtttgcc atcatctccc ca
52952DNAArtificial SequenceContext sequence of SNP rs1569284
9aaagtctatt tattttccca actaatrtgt gtatgcttca tgagagcaca gc
521052DNAArtificial SequenceContext sequence of SNP rs1597611
10agtgagaagt tatggtgctt tctctcrcct gcctatggct gcccacagtc ca
521152DNAArtificial SequenceContext sequence of SNP rs16916456
11tgatgtcagt gattgtaact gtcattycta gaacttgtgt ggttcttttc at
521252DNAArtificial SequenceContext sequence of SNP rs2076683
12aatgaactga cactgcacaa caagackgtc acataaaacc acaggaatca ct
521352DNAArtificial SequenceContext sequence of SNP rs2077642
13ctcctgtagg aggcttgagc ctgggtytag gttggagaca gaggccgaga ag
521452DNAArtificial SequenceContext sequence of SNP rs2382104
14atggacagcc tataggctgt aacctgktat taggaataaa gctttcccta ta
521552DNAArtificial SequenceContext sequence of SNP rs260969
15gtaaaccctc ctcctctttt aagtgcygcc tcctctgtcc ttaatgcccc ca
521652DNAArtificial SequenceContext sequence of SNP rs2695112
16tcatcccccc atgtcgacct aaaagarcac agtttacttt ttcaaggttc tc
521752DNAArtificial SequenceContext sequence of SNP rs314253
17aagttgagag tttcatgcaa aagaccracc caggggtagt gattctgtgg at
521852DNAArtificial SequenceContext sequence of SNP rs35284
18cgttgtaatt ctatcttcag aatgatrcat tgcaaaagag tgtgacaaaa gg
521939DNAArtificial SequenceContext sequence of SNP rs3928471
19gcttcatgtt tatgttctat gttcagyttt tggtctgtt 392052DNAArtificial
SequenceContext sequence of SNP rs4404561 20taccagggcc tgctcagcaa
ccagagmagc agaatggggg gccagatgca ag 522152DNAArtificial
SequenceContext sequence of SNP rs4782109 21atagcaatga tggacaaact
ccctgcytca tggggcttgc attttagagc ta 522252DNAArtificial
SequenceContext sequence of SNP rs636624 22ttggtcactg cctatttcaa
ttctggrttt ctctaacaat gagaaatggt ct 522352DNAArtificial
SequenceContext sequence of SNP rs6574041 23gtagtaggat gtttgaagaa
cagcaargag accagtgtgg ctggaagaag gg 522452DNAArtificial
SequenceContext sequence of SNP rs695083 24actggtttga ggtctccccc
tggagcyacc cagaacacat ccaggcccta cc 522552DNAArtificial
SequenceContext sequence of SNP rs6962352 25gggtagcagt ggctgtccct
gtgggtraag tcacctgacc agccactgtg ag 522652DNAArtificial
SequenceContext sequence of SNP rs7021928 26gaactgtagc aggttctttg
acatgtrtta ttttatatca caacaagcag ag 522752DNAArtificial
SequenceContext sequence of SNP rs712886 27tagtgaataa gagatagact
tggtcaycaa ggagcaatag cttagtgaga ga 522852DNAArtificial
SequenceContext sequence of SNP rs7172184 28agatctagct ctctcaggca
caaacaycca gatattttgt gatagaagga aa 522952DNAArtificial
SequenceContext sequence of SNP rs7225320 29tcccctttct taaggataca
gactttyatc agcagtgatc actcattcac ca 523052DNAArtificial
SequenceContext sequence of SNP rs7534723 30tgtactcttg catgaaacca
ggagaargtt ttacttggtt tgctaaactt tg 523152DNAArtificial
SequenceContext sequence of SNP rs893109 31aatatgcgaa ctttcactta
aaaagtrggg aaaatatagg atctctgaat gc 523252DNAArtificial
SequenceContext sequence of SNP rs9307866 32tagtgcaaga gggggatgtt
tggtatygtg gattgcacag tgaccttgtt tt 523352DNAArtificial
SequenceContext sequence of SNP rs9370809 33ttggagggca tgctggttgc
aaccctytta ttctaataag gaactggttt gg 523452DNAArtificial
SequenceContext sequence of SNP rs957910 34cctcctgaca gtgttgtgtc
ctgtaaytga aagaggattg catctgcact ca 523552DNAArtificial
SequenceContext sequence of SNP rs9837484 35accgacatca gtggtccatt
cagtggrcca ttaatgttgc ctgacattaa gt 523652DNAArtificial
SequenceContext sequence of SNP rs9940922 36tgtagcctcc agggttgctg
tggaagrgaa agagagagca aggagggtct tg 523752DNAArtificial
SequenceContext sequence of SNP rs8123323 37tgatttgaac cgtcaatacc
aaccccytaa ccccagtaaa aaaaaaaaca gc 523852DNAArtificial
SequenceContext sequence of SNP rs12514116 38tggggcctcc tggtgctcct
cagaatyacg tgctctgggc aggaaaaagg tg 523952DNAArtificial
SequenceContext sequence of SNP rs12925135 39tttctaatcc ttacctctca
gagggaygat tatgagaagg aaatgaacta tc 524052DNAArtificial
SequenceContext sequence of SNP rs2663327 40cttgtctctg ccctgaagca
cagccaygtg ggtctgagaa tcccctactt cc 524152DNAArtificial
SequenceContext sequence of SNP rs2367910 41tttttaaatc tttttctttg
tgaacamgta aattgaaatg aaaagctgag ta 524252DNAArtificial
SequenceContext sequence of SNP rs16868972 42aacatcacac tatcaataat
aaggttkaaa ggcctcatgg gaaaagtcct tg 524352DNAArtificial
SequenceContext sequence of SNP rs12535987 43taagttttgt gggcactatt
gataaayaaa atgaaagata aagacaaatg aa 524452DNAArtificial
SequenceContext sequence of SNP rs723811 44ctcacaattc aatgttttag
catataycat caggcaaaac tatcaatttt ga 524552DNAArtificial
SequenceContext sequence of SNP rs11942354 45gtggcggtga ttcccaggtc
tggttgrtca agactgcaat gcacaacagg aa 524652DNAArtificial
SequenceContext sequence of SNP rs6923644 46ggaagtaatt gtattgcatt
aatcagkacc attatttagt attggacatt tc 524752DNAArtificial
SequenceContext sequence of SNP rs1556060 47ctcactcaac aggtttcaat
gggggarcaa ataacaatac gcaaggttaa ta 524852DNAArtificial
SequenceContext sequence of SNP rs1432443 48ccttgtaaca ctacagaagg
atatgtkagg attagagaat tttagcactg ga 524952DNAArtificial
SequenceContext sequence of SNP rs220836 49atatatttta cagtagttgt
caatctrttt tccagttttt ctggtacttt tt 525052DNAArtificial
SequenceContext sequence of SNP rs7974275 50taatgtaaca accaactggt
ctccatktcc ttataggatt aaaagctatt aa 525152DNAArtificial
SequenceContext sequence of SNP rs12275631 51atatagtgag aaatattact
gatgagygga ctgaaactgt tcattgcata tt 525252DNAArtificial
SequenceContext sequence of SNP rs298542 52caacgggtag gaaaggacag
ttggttyagt gcttgctcat gttagccctg ta 525352DNAArtificial
SequenceContext sequence of SNP rs6063144 53actgccgtaa atcactgact
ttgagcytca gcttccctgt ctgtaaaaac ac 525452DNAArtificial
SequenceContext sequence of SNP rs2820100 54atgtcattag tctttgacca
atatttmtcc agtccttatc cagccccagt tc 525552DNAArtificial
SequenceContext sequence of SNP rs7075400 55gtttggggaa tatgttttta
gaaatayaca tgccatatgt gaggctatag aa 525652DNAArtificial
SequenceContext sequence of SNP rs946630 56tctttgggga actgaaagaa
gccctgraga acacaatata caatggcaca cc 525752DNAArtificial
SequenceContext sequence of SNP rs10500866 57cagagctggg catatacagt
aggagaygtt tgctatattt taggtaatta at 525852DNAArtificial
SequenceContext sequence of SNP rs6872664 58tgcttttctg aactaggatc
agatctytcc agcctaaagt ccctccactt tc 525952DNAArtificial
SequenceContext sequence of SNP rs2278556 59ttacgtgcct atcatccagc
tttgtarcat cttaacatta tgccgtactt gc 526052DNAArtificial
SequenceContext sequence of SNP rs1861972 60agaggcgagg tcaccactcc
ctgccartgg ccttgccccc ttcttccccc ac 526152DNAArtificial
SequenceContext sequence of SNP rs7766973 61cccagagggt ttatatttta
cctgcaytcc tgaggatgtg tttgtgttgc tt 526252DNAArtificial
SequenceContext sequence of SNP rs12410279 62agtactgcaa aacaggacag
ccatcaraga ttcttccctg atgacatctc ag 526352DNAArtificial
SequenceContext sequence of SNP rs5918 63ggctcctgtc ttacaggccc
tgcctcyggg ctcacctcgc tgtgacctga ag 526452DNAArtificial
SequenceContext sequence of SNP rs7794745 64acaggtcagg acctggaaag
gcctaawtga taagactaag tgtcaaaatc ag 526552DNAArtificial
SequenceContext sequence of SNP rs10951154 65tctggtaggt agccggctgg
gggtggygat ggtggtggtg gtggtggtgg tg 52
* * * * *
References